def test_individual():
# This needs to be hidden
orig_priors = priors.load_std_priors()
config = getattr(frbs, 'FRB20180924'.lower())
frbA = frbassociate.run_individual(config, orig_priors['adopted'])
# Test
assert isinstance(frbA.candidates, pandas.DataFrame)
assert frbA.candidates.iloc[0].P_Ox > 0.98
def fig_frb_excess(frb_list=None, outfile='fig_frb_excess.png', verbose=False):
"""
Args:
outfile:
Returns:
"""
sns.set_theme()
sns.set_style('whitegrid')
sns.set_context('paper')
if frb_list is None:
frb_list = associate_defs.frb_list
# Plot
plt.figure(figsize=(8, 8))
ax = plt.gca()
# Loop on FRBs
all_seps = []
for ss, frb_name in enumerate(frb_list):
config = getattr(frbs, frb_name.lower())
config['skip_bayesian'] = True
# Go to 30''
max = 60.
config['max_radius'] = max
config['cut_size'] = max
config['cand_separation'] = max * units.arcsec
#
try:
frbA = frbassociate.run_individual(config)
except:
embed(header='fail')
# Save
all_seps += frbA.candidates.separation.values.tolist()
# Histogram
_ = sns.histplot(x=all_seps, ax=ax) #, log_scale=(False,True))
ax.set_xlabel(r'Separation (arcsec)')
ax.set_ylabel(r'Number')
# Font size
set_fontsize(ax, 15.)
# End
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def get_candidates():
# Analyze without priors
all_frbAs = []
for ss, frb_name in enumerate(associate_defs.frb_list):
config = getattr(frbs, frb_name.lower())
config['skip_bayesian'] = True
frbA = frbassociate.run_individual(config)
all_frbAs.append(frbA)
# df
df = pandas.DataFrame(dict(frb=associate_defs.frb_list, frbA=all_frbAs))
# Return
return df
def mktab_frb_results(outfile='tab_frb_results.tex', sub=False):
priors = [associate_defs.conservative,
associate_defs.adopted] #, associate_defs.extreme]
# Open
tbfil = open(outfile, 'w')
# Header
#tbfil.write('\\clearpage\n')
tbfil.write('\\startlongtable\n')
tbfil.write('\\begin{deluxetable*}{cccccccccccccccc}\n')
#tbfil.write('\\rotate\n')
tbfil.write('\\tablewidth{0pc}\n')
tbfil.write(
'\\tablecaption{Results for FRB Associations\\label{tab:results}}\n')
tbfil.write('\\tabletypesize{\\footnotesize}\n')
tbfil.write('\\tablehead{\\colhead{FRB} \n')
tbfil.write(
'& \\colhead{RA$_{\\rm cand}$} & \\colhead{Dec$_{\\rm cand}$} \n')
#tbfil.write('& \\colhead{DM$_{\\rm FRB}$}
tbfil.write('& \\colhead{$\\theta$} \n')
tbfil.write('& \\colhead{\\halflight} \n')
tbfil.write('& \\colhead{\\gmag} \n')
tbfil.write('& \\colhead{Filter} & \\colhead{\\pchance} \n')
tbfil.write('& \\colhead{\\PO} & \\colhead{\\POx} \n')
tbfil.write('& \\colhead{\\PU} & \\colhead{\\PUx} \n')
tbfil.write('\\\\')
#tbfil.write("& (deg) & (deg) & ($''$) & & (deg) & (deg) & ($''$) & ($''$) & ($''$) & ($''$) & (mag)\n")
#tbfil.write("\\\\ (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) & (10) & (11) & (12) & (13) & (14) & (15)")
tbfil.write('} \n')
tbfil.write('\\startdata \n')
# Loop on priors
for pp, prior in enumerate(priors):
tbfil.write('\\cutinhead{' + '{}'.format(prior['name']) + '} \n')
# Loop on FRBs
for ss, frb_name in enumerate(associate_defs.frb_list):
# Config
config = getattr(frbs, frb_name.lower())
config['skip_bayesian'] = True
# Could do this outside the prior loop
frbA = frbassociate.run_individual(config)
# Set priors
frbA.calc_priors(prior['U'], method=prior['O'])
prior['theta']['ang_size'] = frbA.candidates.half_light.values
frbA.set_theta_prior(prior['theta'])
# Calculate p(O_i|x)
frbA.calc_POx()
# Reverse Sort
frbA.candidates = frbA.candidates.sort_values('P_Ox',
ascending=False)
# Loop Galaxy
first = True
for cc, cand in frbA.candidates.iterrows():
if first:
sline = frbA.frb.frb_name
first = False
else:
sline = ""
#
sline += '& {:0.4f} & ${:0.4f}$'.format(cand.ra, cand.dec)
sline += '& {:0.1f}'.format(cand.separation)
sline += '& {:0.2f}'.format(cand.half_light)
# m
sline += '& {:0.2f}'.format(cand[frbA.filter])
sline += '& {:s}'.format(frbA.filter.replace('_', '\_'))
# Pchance
sline += '& {:0.4f}'.format(cand.P_c)
# P(M), P(M|x)
sline += '& {:0.4f}'.format(cand.P_O)
sline += '& {:0.4f}'.format(cand.P_Ox)
# P(S), P(S|X)
sline += '& {:0.4f}'.format(frbA.prior_U)
sline += '& {:0.4f}'.format(frbA.P_Ux)
tbfil.write(sline + '\\\\ \n')
# End end
tbfil.write('\\hline \n')
tbfil.write('\\enddata \n')
#tbfil.write('\\tablenotetext{a}{Spectroscopic redshifts are reported to 4 significant digits. Photometric to 2.} \n')
#tbfil.write('the gas below the line $\\rm DEC_{\\rm off} = \\aslope RA_{\\rm off} \\ayoff$}\n')
#tbfil.write('\\tablecomments{Column 1: FRB source. Columns 2 and 3: R.A. and Decl. of the FRB (J2000). Column 4: FRB error ellipse. Column 5: FRB classication. Repeating = yes(y)/no(n). Column 6 and 7: R.A. and Dec. of the associated host galaxy (J2000). Column 8: projected angular offset of the FRB to the host galaxy center. Column 9: association radius $\delta x$ \citep{Tunnicliffe14}. Column 10: angular effective radius of the host measured from a sersic model using GALFIT \citep{galfit} on the $i$-band images (or equivalent). Column 11: effective search radius \citep{Bloom02}. Column 12: measured apparent magnitude of the host. Column 13: filter used for the magnitude measurement. Column 14: probability of chance coincidence using the \citet{Bloom02} formalism. Column 15: sample designations following the criteria outlined in $\S$~\\ref{ssec:associate}.}\n')
# End
tbfil.write('\\end{deluxetable*} \n')
tbfil.close()
print('Wrote {:s}'.format(outfile))
def fig_vary_PU(frb_list=None, outfile='fig_vary_PU.png'):
"""
Args:
outfile:
Returns:
"""
sns.set_theme()
sns.set_style('whitegrid')
sns.set_context('paper')
if frb_list is None:
frb_list = associate_defs.frb_list
# Priors
aprior = associate_defs.adopted.copy()
# Loop on PU
#PUs = [0., 0.1, 0.5, 0.9]
PUs = np.linspace(0., 0.1, 10)
POx_frbs = np.zeros((len(frb_list), len(PUs)))
for ss, frb_name in enumerate(frb_list):
config = getattr(frbs, frb_name.lower())
config['skip_bayesian'] = True
for pp, PU in enumerate(PUs):
frbA = frbassociate.run_individual(config)
frbA.calc_priors(PU, method=aprior['O'])
aprior['theta']['ang_size'] = frbA.candidates['half_light'].values
frbA.set_theta_prior(aprior['theta'])
# Calculate p(O_i|x)
frbA.calc_POx()
# Reverse sort
frbA.candidates = frbA.candidates.sort_values('P_Ox',
ascending=False)
# Save
POx_frbs[ss, pp] = frbA.candidates.P_Ox.values[0]
#if PU > 0.:
#embed(header='613 of figs')
#frbA.candidates[['ra', 'dec', 'P_O', 'P_Ox', 'p_xO']]
# Plot
set_mplrc()
plt.figure(figsize=(15, 5))
ax = plt.gca()
# Colors
cm = plt.get_cmap('jet')
N = len(frb_list) + 1
plt.rcParams["axes.prop_cycle"] = plt.cycler("color",
cm(np.linspace(0, 1, N)))
# Plot
for kk, frb_name in enumerate(frb_list):
sns.lineplot(x=PUs, y=POx_frbs[kk, :], ax=ax, label=frb_name)
# Label me
ax.set_xlabel(r'$P(U)$')
ax.set_ylabel(cPOxi)
#ax.xaxis.set_major_locator(plt.MultipleLocator(1.))
#ax.xaxis.set_major_formatter(FormatStrFormatter(r'$%.3f$'))
ax.set_ylim(0., 1.)
# Legend
legend = ax.legend(loc='upper right',
scatterpoints=1,
borderpad=0.2,
handletextpad=handletextpad,
fontsize='large')
set_fontsize(ax, 13.)
# End
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def fig_galaxy_priors(frb_name, outfile='fig_galaxy_priors.png'):
"""
Args:
frb_name:
outfile:
Returns:
"""
if frb_name == 'FRB180924':
config = frbs.frb180924
elif frb_name == 'FRB190523':
config = frbs.frb190523
else:
raise IOError("Bad FRB")
config['skip_bayesian'] = True
frbA = frbassociate.run_individual(config) #, verbose=True, show=True)
# Pchance
frbA.calc_pchance()
# Priors
# Uniform
P_U = 0
frbA.calc_priors(P_U, method='identical')
frbA.candidates['P_O_u'] = frbA.candidates['P_O'].values.copy()
# Linear
#frbA.calc_priors(P_U, method='linear')
#frbA.candidates['P_O_l'] = frbA.candidates['P_O'].values.copy()
# Inverse
frbA.calc_priors(P_U, method='inverse')
frbA.candidates['P_O_i'] = frbA.candidates['P_O'].values.copy()
fsz = 9.
lsz = fsz
fig = plt.figure(figsize=(3, 6))
# Image
x0, y0 = 0.25, 0.5
aximg = fig.add_axes([x0, y0, 0.95 - x0, 0.98 - y0], projection=frbA.wcs)
blues = plt.get_cmap('Blues')
vmnx = 200.
d = aximg.imshow(frbA.hdu.data, cmap=blues, vmin=-vmnx, vmax=vmnx)
# Mark candidates
markers = ['o', 's', 'D', '^']
ss = 0
for _, cand in frbA.candidates.iterrows():
aximg.plot(cand.ra,
cand.dec,
markers[ss],
color='pink',
transform=aximg.get_transform('icrs'),
ms=2,
alpha=0.5)
ss += 1
# FRB localization
apermap = plot_frb_localization(frbA.frb, frbA.wcs)
apermap.plot(color='red', lw=1, ls='dotted')
plt.grid(color='gray', ls='dashed', lw=0.5)
aximg.set_xlabel('Right Ascension (J2000)', fontsize=fsz)
aximg.set_ylabel('Declination (J2000)', fontsize=fsz, labelpad=-2)
aximg.tick_params(axis='both', labelsize=lsz)
aximg.set_title(frbA.frb.frb_name)
# ###############################################################
# Priors
#
x0, y0 = 0.18, 0.08
axp = fig.add_axes([x0, y0, 0.97 - x0, 0.47 - y0])
clrs = ['g', 'k', 'b']
#for lbl, postfix, clr in zip(['Identical', 'Linear', 'Inverse'],
# ['_u', '_l', '_i'],
# clrs):
for lbl, postfix, clr in zip(['Identical', 'Inverse'], ['_u', '_i'], clrs):
ss = 0
for _, cand in frbA.candidates.iterrows():
_lbl = lbl if ss == 0 else None
axp.scatter(cand.separation,
cand['P_O' + postfix],
marker=markers[ss],
color=clr,
label=_lbl)
ss += 1
axp.set_xlabel(r'$\theta$ (arcsec)')
axp.set_ylabel(cPOi)
axp.set_xlim(0., 10.)
axp.set_ylim(0., 1.)
set_fontsize(axp, 9.)
legend = axp.legend(loc='upper right',
scatterpoints=0,
borderpad=0.2,
handlelength=0,
handletextpad=0,
fontsize='small')
# Color the text
for ss, text in enumerate(legend.get_texts()):
plt.setp(text, color=clrs[ss])
# Turn off dots
for item in legend.legendHandles:
item.set_visible(False)
# End
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def fig_pOx_frbs(frb_list=None,
outfile='fig_pOx_frbs.png',
tmax=None,
exp_offset=False,
verbose=False):
"""
Args:
outfile:
Returns:
"""
if frb_list is None:
frb_list = associate_defs.frb_list
# Priors
#priors = [associate_defs.conservative, associate_defs.adopted, associate_defs.extreme]
priors = [
associate_defs.conservative.copy(),
associate_defs.adopted.copy()
]
# Adjust priors
if tmax is not None:
for prior in priors:
prior['theta']['max'] = tmax
if exp_offset:
for prior in priors:
prior['theta']['method'] = 'exp'
#
clrs = ['g', 'k', 'b']
Npriors = len(priors)
width = 0.9 / Npriors
# Plot
set_mplrc()
plt.figure(figsize=(15, 5))
ax = plt.gca()
ind = np.arange(len(frb_list))
plt.xticks(ind, frb_list)
# Loop on FRBs
for ss, frb_name in enumerate(frb_list):
config = getattr(frbs, frb_name.lower())
config['skip_bayesian'] = True
for pp, prior in enumerate(priors):
frbA = frbassociate.run_individual(config)
frbA.calc_priors(prior['U'], method=prior['O'])
prior['theta']['ang_size'] = frbA.candidates['half_light'].values
frbA.set_theta_prior(prior['theta'])
# Calculate p(O_i|x)
frbA.calc_POx()
# Reverse sort
frbA.candidates = frbA.candidates.sort_values('P_Ox',
ascending=False)
cum_POx = 0
for cc, cand in frbA.candidates.iterrows():
# Bar me!
ax.bar(ss - 0.45 + (pp + 0.5) * width,
cand.P_Ox,
width,
color=clrs[pp],
bottom=cum_POx,
edgecolor=clrs[pp],
fill=(cum_POx == 0))
cum_POx += cand.P_Ox
# Print
if verbose:
print(
frb_name + '\n', frbA.candidates[[
'id', frbA.filter, 'half_light', 'separation', 'P_O',
'P_Ox'
]])
# Label me
#ax.set_xlabel(r'$\theta/\theta_{1/2}$')
ax.set_ylabel(cPOxi)
#ax.xaxis.set_major_locator(plt.MultipleLocator(1.))
#ax.xaxis.set_major_formatter(FormatStrFormatter(r'$%.3f$'))
ax.set_ylim(0., 1.)
# Legend
legend = ax.legend(loc='upper right',
scatterpoints=1,
borderpad=0.2,
handletextpad=handletextpad,
fontsize='large')
set_fontsize(ax, 13.)
# End
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def fig_pchance(frb_name='FRB180924', outfile='fig_pchance.png'):
"""
Args:
frb_name:
outfile:
"""
set_mplrc()
mpl.rcParams['text.latex.preamble'] = [r'\boldmath']
if frb_name == 'FRB180924':
config = frbs.frb180924
elif frb_name == 'FRB190614':
config = frbs.frb190614
config['cut_size'] = 20.
else:
raise IOError("Bad FRB")
config['skip_bayesian'] = True
frbA = frbassociate.run_individual(config) #, verbose=True, show=True)
fsz = 9.
lsz = fsz
fig = plt.figure(figsize=(5, 5))
# Image
x0, y0 = 0.25, 0.1
aximg = fig.add_axes([x0, y0, 0.95 - x0, 0.98 - y0], projection=frbA.wcs)
#blues = plt.get_cmap('Blues')
greens = plt.get_cmap('Greens')
vmnx = 200.
d = aximg.imshow(frbA.hdu.data, cmap=greens, vmin=-vmnx, vmax=vmnx)
# Label candidates
for cc, cand in frbA.candidates.iterrows():
roffset, doffset = 0, 0
if frb_name == 'FRB180924':
doffset = 0.9 / 3600. if cand.separation > 1.5 else -4. / 3600.
if cand.separation < 1.5:
roffset = 10 / 3600.
elif np.abs(getattr(cand, frbA.filter) - 25.5) < 0.1:
roffset = 3 / 3600.
else:
roffset = 1 / 3600.
elif frb_name == 'FRB190614':
doffset = -4. / 3600. if np.abs(cand.separation -
2.18) < 0.1 else 0.9 / 3600.
roffset = 3 / 3600 # if np.abs(cand.separation-2.18) < 0.1 else 0 / 3600.
aximg.text(cand.ra + roffset,
cand.dec + doffset,
r"$m = {:0.1f}$".format(getattr(cand, frbA.filter)) + "\n" +
r"$" + cmhalf + "={:0.1f}''$".format(cand.half_light) +
"\n" + r"$\theta={:0.1f}''$".format(cand.separation) +
"\n" + r"$P^c={:0.2f}$".format(cand.P_c),
color='k',
transform=aximg.get_transform('icrs'),
size=fsz,
ha='left')
#aximg.plot(row['ra'], row['dec'], markers[ss], color='pink',
# transform=aximg.get_transform('icrs'), ms=2,
# alpha=0.5)
# FRB localization
apermap = plot_frb_localization(frbA.frb, frbA.wcs, scale=5.)
apermap.plot(color='red', lw=1, ls='dotted')
plt.grid(color='gray', ls='dashed', lw=0.5)
asz = 11.
aximg.set_xlabel(r'\textbf{Right Ascension (J2000)}', fontsize=asz)
aximg.set_ylabel(r'\textbf{Declination (J2000)}',
fontsize=asz,
labelpad=-1)
aximg.tick_params(axis='both', labelsize=lsz)
aximg.set_title(frbA.frb.frb_name)
# End
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def fig_galaxy_posteriors(frb_name,
outfile='fig_galaxy_posteriors.png',
raoffsets=None,
decoffsets=None,
cmap=None,
frb_color='r',
vmnx=(None, None),
cut_low=False,
add_faint=None,
prior_mode='adopted',
min_POx=0.01):
"""
Args:
frb_name:
outfile:
add_faint (tuple, optional):
separation, half-light, mag
Returns:
"""
if frb_name in ['FRB180301']:
config = analysis.frb180301
else:
config = getattr(frbs, frb_name.lower())
# Run
config['skip_bayesian'] = True
frbA = frbassociate.run_individual(config) #, verbose=True, show=True)
# Add fake?
if add_faint:
fake_coord = frbA.frb.coord.directional_offset_by(
0. * units.deg, add_faint[0] * units.arcsec)
fake_row = frbA.candidates.iloc[0]
# Fill in
fake_row.coords = fake_coord
fake_row.ra = fake_coord.ra.value
fake_row.dec = fake_coord.dec.value
fake_row.separation = add_faint[0]
fake_row.half_light = add_faint[1]
fake_row[frbA.filter] = add_faint[2]
fake_row.id = 99
# Add it in
frbA.candidates = frbA.candidates.append(fake_row, ignore_index=True)
# Redo chance
frbA.calc_pchance()
# Prior
prior = getattr(associate_defs, prior_mode).copy()
# Set priors
frbA.calc_priors(prior['U'], method=prior['O'])
prior['theta']['ang_size'] = frbA.candidates['half_light'].values
frbA.set_theta_prior(prior['theta'])
# Calculate p(O_i|x)
frbA.calc_POx()
frbA.candidates = frbA.candidates.sort_values('P_Ox', ascending=False)
# Plot
fsz = 10.
lsz = fsz
fig = plt.figure(figsize=(5, 5))
# Image
x0, y0 = 0.25, 0.1
aximg = fig.add_axes([x0, y0, 0.95 - x0, 0.98 - y0], projection=frbA.wcs)
if cmap is None:
cmap = plt.get_cmap('Blues')
d = aximg.imshow(frbA.hdu.data, cmap=cmap, vmin=vmnx[0], vmax=vmnx[1])
# Mark candidates
markers = ['o', 's', 'D', '^']
# Label candidates
ss = 0
for cc, cand in frbA.candidates.iterrows():
if cut_low and cand.P_Ox < min_POx:
continue
# Offset
roffset = 0. if raoffsets is None else raoffsets[ss] / 3600.
doffset = 0. if decoffsets is None else decoffsets[ss] / 3600.
#
aximg.text(cand.ra + roffset,
cand.dec + doffset,
r"$P(O)" + "={:0.2f}$".format(cand.P_O) + "\n" +
r"$P(O_i|x) = {:0.2f}$".format(cand.P_Ox),
color='k',
transform=aximg.get_transform('icrs'),
size=fsz,
ha='left')
#
ss += 1
# FRB localization
apermap = plot_frb_localization(frbA.frb, frbA.wcs)
apermap.plot(color=frb_color, lw=1, ls='dashed')
plt.grid(color='gray', ls='dashed', lw=0.5)
aximg.set_xlabel('Right Ascension (J2000)', fontsize=fsz)
aximg.set_ylabel('Declination (J2000)', fontsize=fsz, labelpad=-1)
aximg.tick_params(axis='both', labelsize=lsz)
aximg.set_title(frbA.frb.frb_name)
# End
#plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
def fig_segm(outfile='fig_segm.png'):
"""
Args:
outfile:
Returns:
"""
set_mplrc()
# Plot
plt.figure(figsize=(9, 5))
gs = gridspec.GridSpec(1, 2)
# FRB 180924
ax0 = plt.subplot(gs[0])
config0 = frbs.frb180924
frbA_180924 = frbassociate.run_individual(config0)
cmap = frbA_180924.segm.make_cmap()
ax0.imshow(frbA_180924.segm,
origin='lower',
cmap=cmap,
interpolation='nearest',
extent=[
-config0['cut_size'] / 2, config0['cut_size'] / 2,
-config0['cut_size'] / 2, config0['cut_size'] / 2
])
ax0.set_title('FRB 180924')
# FRB localization
apermap = plot_frb_localization(frbA_180924.frb, frbA_180924.wcs)
apermap.plot(color='white', lw=1)
# FRB 190523
ax1 = plt.subplot(gs[1])
config1 = frbs.frb190523
frbA_190523 = frbassociate.run_individual(config1)
cmap = frbA_190523.segm.make_cmap()
ax1.imshow(frbA_190523.segm,
origin='lower',
cmap=cmap,
interpolation='nearest',
extent=[
-config1['cut_size'] / 2, config1['cut_size'] / 2,
-config1['cut_size'] / 2, config1['cut_size'] / 2
])
ax1.set_title('FRB 190523')
apermap = plot_frb_localization(frbA_190523.frb, frbA_190523.wcs)
apermap.plot(color='white', lw=1)
# Label me
for ax in [ax0, ax1]:
ax.set_xlabel('arcsec')
ax.set_ylabel('arcsec')
#ax.xaxis.set_major_locator(plt.MultipleLocator(1.))
#ax.xaxis.set_major_formatter(FormatStrFormatter(r'$%.3f$'))
# Font size
for ax in [ax0, ax1]:
set_fontsize(ax, 15.)
# End
plt.tight_layout(pad=0.2, h_pad=0., w_pad=0.1)
print('Writing {:s}'.format(outfile))
kwargs = {}
if 'png' in outfile:
kwargs['dpi'] = 700
plt.savefig(outfile, **kwargs)
plt.close()
from frb.associate import frbassociate from frb.associate import frbs from IPython import embed """ RUN THIS """ #frb_name = 'FRB121102' #frb_name = 'FRB180916' frb_name = 'FRB180924' #frb_name = 'FRB190611' #frb_name = 'FRB190614' #frb_name = 'FRB181112' #frb_name = 'FRB190102' #frb_name = 'FRB190711' #frb_name = 'FRB190714' #frb_name = 'FRB191001' #frb_name = 'FRB200430' config = getattr(frbs, frb_name.lower()) config['skip_bayesian'] = True frbA = frbassociate.run_individual(config, show=True, verbose=True) embed()
def run(frb_list: list,
host_coords: list,
prior: dict,
override: bool = False):
"""Main method for generating a Host JSON file
Args:
frb_list (list): List of FRB names from the database
host_coords (list): List of host galaxy coords fom the database
prior (dict):
Prior for PATH
override (bool, optional): Attempt to over-ride errors.
Mainly for time-outs of public data. Defaults to False.
tol (float, optional): Tolerance for a match to the expected host
in arcsec.
Raises:
e: [description]
ValueError: [description]
Returns:
pandas.DataFrame: Table of PATH values and a bit more
"""
good_frb, PATH_O, PATH_Ox, RAs, Decs = [], [], [], [], []
ang_sizes, separations, sep_err = [], [], []
skipped = []
for frb, host_coord in zip(frb_list, host_coords):
frb_name = utils.parse_frb_name(frb, prefix='frb')
# Config
if not hasattr(frbs, frb_name.upper()):
print(f"PATH analysis not possible for {frb_name}")
continue
print(f"Performing PATH on {frb_name}")
config = getattr(frbs, frb_name.upper())
# Run me
frbA = frbassociate.run_individual(config, prior=prior)
if frbA is None:
print(f"PATH analysis not possible for {frb_name}")
skipped.append(frb_name)
continue
# Save for table
good_frb.append(frb_name.upper())
PATH_Ox.append(frbA.candidates.P_Ox.values[0])
PATH_O.append(frbA.candidates.P_O.values[0])
RAs.append(host_coord.ra.deg)
Decs.append(host_coord.dec.deg)
ang_sizes.append(frbA.candidates.ang_size.values[0])
separations.append(frbA.candidates.separation.values[0])
# Build the table
df = pandas.DataFrame()
df['FRB'] = good_frb
df['RA'] = RAs
df['Dec'] = Decs
df['ang_size'] = ang_sizes
df['P_O'] = PATH_O
df['P_Ox'] = PATH_Ox
df['separation'] = separations
for frb_name in skipped:
print(f"PATH analysis not possible for {frb_name}")
#
return df