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)
count_records = 0
progress.max = len(opts.fitsfilenames)
for record in map(process, opts.fitsfilenames):
count_records += 1
progress.update(count_records, record[0])
print(*record, sep="\t", file=opts.output)
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.max = len(fitsfilenames)
for record in map(process, fitsfilenames):
count_records += 1
progress.update(count_records, record[0])
print(*record, sep="\t", file=opts.output)
bin_edges = 10.**log10_far
bin_names = ['far_1e{0}'.format(e) for e in log10_far]
bin_titles = [
r'$\mathrm{{FAR}} \leq 10^{{{0}}}$ Hz'.format(e) for e in log10_far
]
elif opts.group_by == 'snr':
combined = np.concatenate([dataset['snr'] for dataset in datasets_])
min_snr = int(np.floor(np.min(combined)))
max_snr = int(np.floor(np.max(combined)))
bin_edges = np.arange(min_snr, max_snr + 1)
bin_names = ['snr_{0}'.format(e) for e in bin_edges]
bin_titles = [r'$\mathrm{{SNR}} \geq {0}$'.format(e) for e in bin_edges]
# Set maximum range of progress bar: one tick for each of 5 figures, for each
# false alarm rate bin.
pb.max = len(bin_edges) * 4
if opts.cumulative:
histlabel = 'cumulative '
else:
histlabel = ''
if opts.normed:
histlabel += 'fraction'
else:
histlabel += 'number'
histlabel += ' of injections'
# Loop over false alarm rate bins.
for i, (bin_edge, subdir,
title) in enumerate(zip(bin_edges, bin_names, bin_titles)):
pb.update(text=subdir)
log10_far = np.arange(log10_min_far, log10_max_far + 1)
bin_edges = 10.**log10_far
bin_names = ['far_1e{0}'.format(e) for e in log10_far]
bin_titles = [r'$\mathrm{{FAR}} \leq 10^{{{0}}}$ Hz'.format(e) for e in log10_far]
elif opts.group_by == 'snr':
combined = np.concatenate([dataset['snr'] for dataset in datasets_])
min_snr = int(np.floor(np.min(combined)))
max_snr = int(np.floor(np.max(combined)))
bin_edges = np.arange(min_snr, max_snr + 1)
bin_names = ['snr_{0}'.format(e) for e in bin_edges]
bin_titles = [r'$\mathrm{{SNR}} \geq {0}$'.format(e) for e in bin_edges]
# Set maximum range of progress bar: one tick for each of 5 figures, for each
# false alarm rate bin.
pb.max = len(bin_edges) * 4
if opts.cumulative:
histlabel = 'cumulative '
else:
histlabel = ''
if opts.normed:
histlabel += 'fraction'
else:
histlabel += 'number'
histlabel += ' of injections'
# Loop over false alarm rate bins.
for i, (bin_edge, subdir, title) in enumerate(zip(bin_edges, bin_names, bin_titles)):
pb.update(text=subdir)
import healpy as hp
import lal
from lalinference.io import fits
from lalinference import plot
from glue.text_progress_bar import ProgressBar
fig = plt.figure(frameon=False)
ax = plt.axes(projection='mollweide')
ax.grid()
progress = ProgressBar()
progress.update(-1, 'obtaining filenames of sky maps')
fitsfilenames = tuple(command.chainglob(opts.fitsfileglobs))
progress.max = len(fitsfilenames)
matplotlib.rc('path', simplify=True, simplify_threshold=1)
if opts.colormap is None:
colors = ['k'] * len(fitsfilenames)
else:
colors = matplotlib.cm.get_cmap(opts.colormap)
colors = colors(np.linspace(0, 1, len(fitsfilenames)))
for count_records, (color,
fitsfilename) in enumerate(zip(colors, fitsfilenames)):
progress.update(count_records, fitsfilename)
skymap, metadata = fits.read_sky_map(fitsfilename, nest=None)
nside = hp.npix2nside(len(skymap))
gmst = lal.GreenwichMeanSiderealTime(metadata['gps_time']) % (2 * np.pi)
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)
count_records = 0
progress.max = len(opts.fitsfilenames)
for record in map(process, opts.fitsfilenames):
count_records += 1
progress.update(count_records, record[0])
print(*record, sep="\t", file=opts.output)
