def test_get_spectra():
"""
Check grabbing spectra from the specDB
Note: This requires that specdb be installed..
"""
try:
import specdb
except ImportError:
assert True
return
# Check for specDB file
if utils.load_specdb() is None:
assert True
return
# Do it!
frb180924 = FRB.by_name('FRB180924')
host180924 = frb180924.grab_host()
meta, xspec = host180924.get_metaspec()
# Test
assert isinstance(meta, Table)
assert isinstance(xspec, xspectrum1d.XSpectrum1D)
assert xspec.nspec == 1
#
meta, xspec = host180924.get_metaspec(instr='MUSE')
# Test
assert isinstance(xspec, xspectrum1d.XSpectrum1D)
assert xspec.nspec == 1
#
meta, xspecs = host180924.get_metaspec(return_all=True)
assert xspecs.nspec == 2
def host_obj():
# VLT
photom = Table()
photom['Name'] = ['G_TEST']
photom['ra'] = 123.422
photom['dec'] = 23.222
# These are observed
photom['LRISb_V'] = 25.86
photom['LRISb_V_err'] = 0.25
photom['GMOS_S_r'] = 23.61
photom['GMOS_S_r_err'] = 0.15
photom['LRISr_I'] = 23.09
photom['LRISr_I_err'] = 0.1
photom['NOT_z'] = 23.35
photom['NOT_z_err'] = 0.3
photom['NIRI_J'] = 21.75 + 0.91
photom['NIRI_J_err'] = 0.2
#
host190613A = FRBHost(photom['ra'], photom['dec'],
FRB.by_name('FRB20121102'))
host190613A.parse_photom(photom)
host190613A.name = 'G_TEST'
return host190613A
def test_luminosity():
frb121102 = FRB.by_name('FRB121102')
host121102 = frbgalaxy.FRBHost.from_json(frb121102, data_path('test_frbhost.json'))
Lum_Ha, Lum_Ha_err = host121102.calc_nebular_lum('Halpha')
# Test
assert Lum_Ha.unit == units.erg/units.s
assert np.isclose(Lum_Ha.value, 2.93961853e+40)
def test_read_frbhost():
# This test will fail if the previous failed
frb121102 = FRB.by_name('FRB121102')
host121102 = frbgalaxy.FRBHost.from_json(frb121102, data_path('test_frbhost.json'))
# Test
assert host121102.frb.frb_name == 'FRB121102'
assert np.isclose(host121102.morphology['b/a'], 0.25)
assert host121102.vet_all()
def test_parse_galfit():
frb = FRB.by_name("FRB20121102A")
host = frb.grab_host()
galfit_outfile = resource_filename('frb',
'tests/files/HG121102_galfit.fits')
# Test two components
host.parse_galfit(galfit_outfile, twocomponent=True)
assert type(host.morphology['PA']) == np.ndarray
assert len(host.morphology['PA']) == 2
# Test a single component
host.parse_galfit(galfit_outfile, twocomponent=False)
assert type(host.morphology['PA']) == np.float64
def test_luminosity():
# This test will fail if the previous failed
outfile = data_path('test_frbhost.json')
frb121102 = FRB.by_name('FRB20121102A')
host121102 = frbgalaxy.FRBHost.from_json(frb121102, outfile)
Lum_Ha, Lum_Ha_err = host121102.calc_nebular_lum('Halpha')
# Test
assert Lum_Ha.unit == units.erg / units.s
assert np.isclose(Lum_Ha.value, 2.9447660789951146e+40)
# Remove
os.remove(outfile)
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 test_offset():
ifrb = FRB.by_name('FRB20121102A')
host = ifrb.grab_host()
ra_sig_source = host.positional_error['ra_source']
ra_sig_astro = host.positional_error['ra_astrometric']
dec_sig_source = host.positional_error['dec_source']
dec_sig_astro = host.positional_error['dec_astrometric']
host_ra_sig = np.sqrt(ra_sig_astro**2 + ra_sig_source**2)
host_dec_sig = np.sqrt(dec_sig_astro**2 + dec_sig_source**2)
ang_avg, avg_err, ang_best, best_err = frb_offsets.angular_offset(
ifrb, host, gal_sig=(host_ra_sig, host_dec_sig))
assert np.isclose(ang_best, 0.22687, rtol=1e-5)
def test_named():
frb121102 = FRB('FRB121102',
'J053158.7+330852.5',
558.1 * units.pc / units.cm**3,
z_frb=0.19273)
# Error ellipse
frb121102.set_ee(0.1, 0.1, 0., 95.)
assert isinstance(frb121102.eellipse, dict)
# Test writing
frb121102.write_to_json()
# Test load
tst = FRB.from_json('FRB121102.json')
# By name
tst = FRB.by_name('FRB121102')
def test_host_build():
outfile = data_path('FRB20180924_host.json')
if os.path.isfile(outfile):
os.remove(outfile)
# Requires a file on disk that is too slow to generate in CI
pargs = build.parser(['Hosts', '--frb', 'FRB20180924'])
frbs = pargs.frb.split(',')
frbs = [ifrb.strip() for ifrb in frbs]
out_path = data_path('')
build_hosts.main(frbs,
options=pargs.options,
hosts_file=pargs.data_file,
lit_refs=pargs.lit_refs,
override=pargs.override,
out_path=out_path)
# Check
frb20180924 = FRB.by_name('FRB20180924')
host = frbgalaxy.FRBHost.from_json(frb20180924, outfile)
# Clean up
os.remove(outfile)
new_pa_gal = pa_gal + dtheta * units.deg
# x, y gal
x_gal = -r.value * np.sin(new_pa_gal).value
y_gal = r.value * np.cos(new_pa_gal).value
# Offset
ang_off = np.sqrt((xx - x_gal)**2 + (yy - y_gal)**2)
p_xy = np.exp(-xx**2 / (2 * sig_a**2)) * np.exp(-yy**2 / (2 * sig_b**2))
# Best offset
best_off = r.value
var_best = np.sum((ang_off - best_off)**2 * p_xy) / np.sum(p_xy)
sig_best = np.sqrt(var_best)
# Average over the grid
avg_off = np.sum(ang_off * p_xy) / np.sum(p_xy)
var_off = np.sum((ang_off - avg_off)**2 * p_xy) / np.sum(p_xy)
sig_off = np.sqrt(var_off)
# Return
return avg_off, sig_off, best_off, sig_best
if __name__ == '__main__':
from frb.frb import FRB
ifrb = FRB.by_name('FRB200430')
host = ifrb.grab_host()
angular_offset(ifrb, host)
def build_fg_181112(build_photom=False):
"""
Data taken from Prochaska et al. 2019, Science, in press
Args:
build_photom (bool, optional):
Generate the photometry table
"""
# Coord from DES
fg_coord = SkyCoord('J214923.89-525810.43', unit=(units.hourangle, units.deg)) # from DES
frb181112 = FRB.by_name('FRB181112')
# Instantiate
fg_13_5 = frbgalaxy.FGGalaxy(fg_coord.ra.value, fg_coord.dec.value, '181112')
fg_13_5.frb_coord = frb181112.coord
# Redshift
fg_13_5.set_z(0.36738, 'spec', err=7e-5)
# Photometry
photom_file = os.path.join(db_path, 'CRAFT', 'Prochaska2019', 'prochaska2019_photom.ascii')
if build_photom:
# DES
# Grab the table (requires internet)
search_r = 2 * units.arcsec
des_srvy = des.DES_Survey(fg_coord, search_r)
des_tbl = des_srvy.get_catalog(print_query=True)
fg_13_5.parse_photom(des_tbl)
# VLT -- Lochlan 2019-05-02
# VLT -- Lochlan 2019-06-18
fg_13_5.photom['VLT_g'] = 21.20
fg_13_5.photom['VLT_g_err'] = 0.04
fg_13_5.photom['VLT_I'] = 19.20
fg_13_5.photom['VLT_I_err'] = 0.02
# Build a Table
photom = Table()
photom['Name'] = ['FG181112_13_5'] # JXP internal name
photom['ra'] = fg_13_5.coord.ra.value
photom['dec'] = fg_13_5.coord.dec.value
# Add in
for key in fg_13_5.photom.keys():
photom[key] = fg_13_5.photom[key]
# Merge
# Merge/write
photom = frbphotom.merge_photom_tables(photom, photom_file)
photom.write(photom_file, format=frbphotom.table_format, overwrite=True)
# Either way read it
fg_13_5.parse_photom(Table.read(photom_file, format=frbphotom.table_format))
# Nebular lines
fg_13_5.parse_ppxf(os.path.join(db_path, 'CRAFT', 'Prochaska2019', 'FG181112_13_5_FORS2_ppxf.ecsv'))
# Derived quantities
fg_13_5.calc_nebular_AV('Ha/Hb')
# This will be an upper limit
fg_13_5.calc_nebular_SFR('Ha')
fg_13_5.derived['SFR_nebular_err'] = -999.
# CIGALE
fg_13_5.parse_cigale(os.path.join(db_path, 'CRAFT', 'Prochaska2019', 'FG181112_13_5_CIGALE.fits'))
# Write
path = resource_filename('frb', 'data/Galaxies/181112')
fg_13_5.write_to_json(path=path)
def _instantiate_intepolators(datafolder: str = DEFAULT_DATA_FOLDER,
dmfilename: str = None,
frb_name: str = "FRB180924") -> list:
"""
Produce interpolator functions
for key quantities required
for the analysis.
Args:
datfolder(str, optional): Folder where the interpolation data files exist
dmfilename(str, optional): file name (within datafolder) for the DM interpolation data.
frb_name(str, optional): Assumes "FRB180924" by default.
Returns:
dm_interpolator (RegularGridInterpolator): DM(z, offset_kpc, log_mhalo)
mean_interp (interp2d): <log_mhalo(log_mstar, z)> (based on SHMR)
stddev_interp (interp2d): std.dev. log_mhalo(log_mstar, z) (based on SHMR)
ang_dia_interp (interp1d): angular_diameter_distance(z) (default Repo cosmology)
"""
# DM for a variety of halo parameters.
if not dmfilename:
dmfilename = "halo_dm_data.npz"
dmdata = np.load(dmfilename)
redshifts = dmdata['redshifts']
offsets = dmdata['offsets']
log_mhalos = dmdata['m_halo']
dm_grid = dmdata['dm']
dm_interpolator = RegularGridInterpolator((redshifts, offsets, log_mhalos),
dm_grid,
bounds_error=False,
fill_value=0.)
# Halo mass mean and variance from stellar mass
frb = FRB.by_name(frb_name)
realization_files = glob.glob(
os.path.join(datafolder, "mhalo_realization_z*.npz"))
realization_files.sort()
# Define redshift grid
zgrid = np.linspace(0, frb.z, 10)
# Now initialize arrays to store mean and std.dev.
mean_arrays = []
stddev_arrays = []
# Loop through files, compute mean & std.dev of log_mhalo for log_mstar
for file in realization_files:
loaded = np.load(file)
log_mhalo = loaded['MHALO']
mean_mhalo, _, stddev_mhalo = sigma_clipped_stats(log_mhalo,
sigma=20,
axis=1)
mean_arrays.append(mean_mhalo)
stddev_arrays.append(stddev_mhalo)
# laoded is going to be from the last file in the loop. The first entry contains
# a stellar mass array.
log_mstar = loaded['MSTAR']
mean_interp = interp2d(log_mstar,
zgrid,
np.array(mean_arrays),
bounds_error=False)
stddev_interp = interp2d(log_mstar,
zgrid,
np.array(stddev_arrays),
bounds_error=False)
# Angular diameter distance
z = np.linspace(0, 7, 10000)
ang_dia_dist = defs.frb_cosmo.angular_diameter_distance(z).to('kpc').value
ang_dia_interp = interp1d(z,
ang_dia_dist,
bounds_error=False,
fill_value='extrapolate')
# Return interpolators
return dm_interpolator, mean_interp, stddev_interp, ang_dia_interp