import matplotlib
import matplotlib.pyplot as plt
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))
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.max = len(fitsfilenames)
for record in map(process, fitsfilenames):
count_records += 1
progress.update(count_records, record[0])
import healpy as hp
import lal
from lalinference import fits
from lalinference import plot
from glue.text_progress_bar import ProgressBar
fig = plt.figure(figsize=(opts.figure_width, opts.figure_height), frameon=False)
ax = plt.subplot(111, projection='mollweide')
ax.cla()
ax.grid()
progress = ProgressBar()
progress.update(-1, 'obtaining filenames of sky maps')
fitsfilenames = tuple(command.chainglob(args))
progress.max = len(fitsfilenames)
matplotlib.rc('path', simplify=True, simplify_threshold=1)
for count_records, fitsfilename in enumerate(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)
indices = np.argsort(-skymap)
region = np.empty(skymap.shape)
region[indices] = 100 * np.cumsum(skymap[indices])
plot.healpix_contour(