def process(fitsfilename):
sky_map = fits.read_sky_map(fitsfilename, moc=True)
coinc_event_id = sky_map.meta['objid']
try:
runtime = sky_map.meta['runtime']
except KeyError:
runtime = float('nan')
contour_pvalues = 0.01 * np.asarray(contours)
row = db.execute(
"""
SELECT DISTINCT sim.simulation_id AS simulation_id,
sim.longitude AS ra, sim.latitude AS dec, sim.distance AS distance,
ci.combined_far AS far, ci.snr AS snr
FROM coinc_event_map AS cem1 INNER JOIN coinc_event_map AS cem2
ON (cem1.coinc_event_id = cem2.coinc_event_id)
INNER JOIN sim_inspiral AS sim ON (cem1.event_id = sim.simulation_id)
INNER JOIN coinc_inspiral AS ci ON (cem2.event_id = ci.coinc_event_id)
WHERE cem1.table_name = 'sim_inspiral'
AND cem2.table_name = 'coinc_event' AND cem2.event_id = ?
""", (coinc_event_id,)).fetchone()
if row is None:
raise ValueError(
"No database record found for event '{0}' in '{1}'".format(
coinc_event_id, command.sqlite_get_filename(db)))
simulation_id, true_ra, true_dec, true_dist, far, snr = row
searched_area, searched_prob, offset, searched_modes, contour_areas, \
area_probs, contour_modes, searched_prob_dist, contour_dists, \
searched_vol, searched_prob_vol, contour_vols = find_injection_moc(
sky_map, true_ra, true_dec, true_dist, contours=contour_pvalues,
areas=areas, modes=modes)
if snr is None:
snr = np.nan
if far is None:
far = np.nan
distmean = sky_map.meta.get('distmean', np.nan)
diststd = sky_map.meta.get('diststd', np.nan)
log_bci = sky_map.meta.get('log_bci', np.nan)
log_bsn = sky_map.meta.get('log_bsn', np.nan)
ret = [coinc_event_id, simulation_id, far, snr, searched_area,
searched_prob, searched_prob_dist, searched_vol, searched_prob_vol,
offset, runtime, distmean, diststd, log_bci, log_bsn] \
+ contour_areas + area_probs + contour_dists + contour_vols
if modes:
ret += [searched_modes] + contour_modes
return ret
def process(fitsfilename):
sky_map = fits.read_sky_map(fitsfilename, moc=True)
coinc_event_id = sky_map.meta['objid']
try:
runtime = sky_map.meta['runtime']
except KeyError:
runtime = float('nan')
contour_pvalues = 0.01 * np.asarray(contours)
row = db.execute(
"""
SELECT DISTINCT sim.simulation_id AS simulation_id,
sim.longitude AS ra, sim.latitude AS dec, sim.distance AS distance,
ci.combined_far AS far, ci.snr AS snr
FROM coinc_event_map AS cem1 INNER JOIN coinc_event_map AS cem2
ON (cem1.coinc_event_id = cem2.coinc_event_id)
INNER JOIN sim_inspiral AS sim ON (cem1.event_id = sim.simulation_id)
INNER JOIN coinc_inspiral AS ci ON (cem2.event_id = ci.coinc_event_id)
WHERE cem1.table_name = 'sim_inspiral'
AND cem2.table_name = 'coinc_event' AND cem2.event_id = ?
""", (coinc_event_id, )).fetchone()
if row is None:
raise ValueError(
"No database record found for event '{0}' in '{1}'".format(
coinc_event_id, command.sqlite_get_filename(db)))
simulation_id, true_ra, true_dec, true_dist, far, snr = row
searched_area, searched_prob, offset, searched_modes, contour_areas, \
area_probs, contour_modes, searched_prob_dist, contour_dists, \
searched_vol, searched_prob_vol, contour_vols = find_injection_moc(
sky_map, true_ra, true_dec, true_dist, contours=contour_pvalues,
areas=areas, modes=modes)
if snr is None:
snr = np.nan
if far is None:
far = np.nan
distmean = sky_map.meta.get('distmean', np.nan)
diststd = sky_map.meta.get('diststd', np.nan)
log_bci = sky_map.meta.get('log_bci', np.nan)
log_bsn = sky_map.meta.get('log_bsn', np.nan)
ret = [coinc_event_id, simulation_id, far, snr, searched_area,
searched_prob, searched_prob_dist, searched_vol, searched_prob_vol,
offset, runtime, distmean, diststd, log_bci, log_bsn] \
+ contour_areas + area_probs + contour_dists + contour_vols
if modes:
ret += [searched_modes] + contour_modes
return ret
def process(fitsfilename):
sky_map, metadata = fits.read_sky_map(fitsfilename, nest=None)
coinc_event_id = metadata['objid']
try:
runtime = metadata['runtime']
except KeyError:
runtime = float('nan')
row = db.execute(
"""
SELECT DISTINCT sim.simulation_id AS simulation_id, sim.longitude AS ra, sim.latitude AS dec,
ci.combined_far AS far, ci.snr AS snr
FROM coinc_event_map AS cem1 INNER JOIN coinc_event_map AS cem2
ON (cem1.coinc_event_id = cem2.coinc_event_id)
INNER JOIN sim_inspiral AS sim ON (cem1.event_id = sim.simulation_id)
INNER JOIN coinc_inspiral AS ci ON (cem2.event_id = ci.coinc_event_id)
WHERE cem1.table_name = 'sim_inspiral'
AND cem2.table_name = 'coinc_event' AND cem2.event_id = ?""",
(coinc_event_id, )).fetchone()
if row is None:
raise ValueError(
"No database record found for event '{0}' in '{1}'".format(
coinc_event_id, command.sqlite_get_filename(db)))
simulation_id, true_ra, true_dec, far, snr = row
searched_area, searched_prob, offset, searched_modes, contour_areas, area_probs, contour_modes = postprocess.find_injection(
sky_map,
true_ra,
true_dec,
contours=[0.01 * p for p in contours],
areas=areas,
modes=modes,
nest=metadata['nest'])
if snr is None:
snr = float('nan')
if far is None:
far = float('nan')
distmean = metadata.get('distmean', float('nan'))
diststd = metadata.get('diststd', float('nan'))
ret = [
coinc_event_id, simulation_id, far, snr, searched_area, searched_prob,
offset, runtime, distmean, diststd
] + contour_areas + area_probs
if modes:
ret += [searched_modes] + contour_modes
return ret
def process(fitsfilename):
sky_map, metadata = fits.read_sky_map(fitsfilename, nest=None)
coinc_event_id = metadata['objid']
try:
runtime = metadata['runtime']
except KeyError:
runtime = float('nan')
row = db.execute("""
SELECT DISTINCT sim.simulation_id AS simulation_id, sim.longitude AS ra, sim.latitude AS dec,
ci.combined_far AS far, ci.snr AS snr
FROM coinc_event_map AS cem1 INNER JOIN coinc_event_map AS cem2
ON (cem1.coinc_event_id = cem2.coinc_event_id)
INNER JOIN sim_inspiral AS sim ON (cem1.event_id = sim.simulation_id)
INNER JOIN coinc_inspiral AS ci ON (cem2.event_id = ci.coinc_event_id)
WHERE cem1.table_name = 'sim_inspiral'
AND cem2.table_name = 'coinc_event' AND cem2.event_id = ?""",
(coinc_event_id,)).fetchone()
if row is None:
raise ValueError(
"No database record found for event '{0}' in '{1}'".format(
coinc_event_id, command.sqlite_get_filename(db)))
simulation_id, true_ra, true_dec, far, snr = row
searched_area, searched_prob, offset, searched_modes, contour_areas, area_probs, contour_modes = postprocess.find_injection(
sky_map, true_ra, true_dec, contours=[0.01 * p for p in contours],
areas=areas, modes=modes, nest=metadata['nest'])
if snr is None:
snr = float('nan')
if far is None:
far = float('nan')
distmean = metadata.get('distmean', float('nan'))
diststd = metadata.get('diststd', float('nan'))
ret = [coinc_event_id, simulation_id, far, snr, searched_area, searched_prob, offset, runtime, distmean, diststd] + contour_areas + area_probs
if modes:
ret += [searched_modes] + contour_modes
return ret
db = opts.db
contours = opts.contour
modes = opts.modes
areas = opts.area
progress.update(-1, 'spawning workers')
if opts.jobs == 1:
from six.moves import map
else:
try:
from emcee.interruptible_pool import InterruptiblePool as Pool
except ImportError:
from multiprocessing import Pool
map = Pool(
opts.jobs, startup,
(command.sqlite_get_filename(db), contours, modes, areas)).imap
colnames = ([
'coinc_event_id', 'simulation_id', 'far', 'snr', 'searched_area',
'searched_prob', 'searched_prob_dist', 'searched_vol',
'searched_prob_vol', 'offset', 'runtime', 'distmean', 'diststd',
'log_bci', 'log_bsn'
] + ['area({0:g})'.format(_)
for _ in contours] + ['prob({0:g})'.format(_) for _ in areas] +
['dist({0:g})'.format(_) for _ in contours] +
['vol({0:g})'.format(_) for _ in contours])
if modes:
colnames += ['searched_modes']
colnames += ["modes({0:g})".format(p) for p in contours]
print(*colnames, sep="\t", file=opts.output)
db = opts.db
contours = opts.contour
modes = opts.modes
areas = opts.area
progress.update(-1, 'spawning workers')
if opts.jobs == 1:
from six.moves import map
else:
try:
from emcee.interruptible_pool import InterruptiblePool as Pool
except ImportError:
from multiprocessing import Pool
map = Pool(
opts.jobs, startup,
(command.sqlite_get_filename(db), contours, modes, areas)
).imap_unordered
progress.update(-1, 'obtaining filenames of sky maps')
fitsfilenames = tuple(command.chainglob(opts.fitsfileglobs))
colnames = (
['coinc_event_id', 'simulation_id', 'far', 'snr', 'searched_area',
'searched_prob', 'offset', 'runtime', 'distmean', 'diststd'] +
["area({0:g})".format(p) for p in contours] +
["prob({0:g})".format(a) for a in areas])
if modes:
colnames += ['searched_modes'] + ["modes({0:g})".format(p) for p in contours]
print(*colnames, sep="\t", file=opts.output)
count_records = 0
progress = ProgressBar()
db = opts.db
contours = opts.contour
modes = opts.modes
areas = opts.area
progress.update(-1, 'spawning workers')
if opts.jobs == 1:
from six.moves import map
else:
try:
from emcee.interruptible_pool import InterruptiblePool as Pool
except ImportError:
from multiprocessing import Pool
map = Pool(opts.jobs, startup, (command.sqlite_get_filename(db),
contours, modes, areas)).imap_unordered
progress.update(-1, 'obtaining filenames of sky maps')
fitsfilenames = tuple(command.chainglob(opts.fitsfileglobs))
colnames = ([
'coinc_event_id', 'simulation_id', 'far', 'snr', 'searched_area',
'searched_prob', 'offset', 'runtime', 'distmean', 'diststd'
] + ["area({0:g})".format(p)
for p in contours] + ["prob({0:g})".format(a) for a in areas])
if modes:
colnames += ['searched_modes'
] + ["modes({0:g})".format(p) for p in contours]
print(*colnames, sep="\t", file=opts.output)