def __main__():
"""
Monitor MultiNest scan via command line arguments.
"""
# Make parser for command line arguments
parser = arg_parser(description="Monitor MultiNest scan")
parser.add_argument("root", help="Prefix of MultiNest output filenames (root)", type=str)
parser.add_argument(
"--tol", dest="tol", help="MultiNest evidence tolerance factor (tol)", type=float, default=0.1, required=False
)
parser.add_argument(
"--maxiter",
dest="maxiter",
help="MultiNest maximum number of iterations (maxiter)",
type=int,
default=float("inf"),
required=False,
)
parser.add_argument(
"--watch",
dest="watch_mode",
help="Whether to watch rather than snapshot",
action="store_true",
default=False,
required=False,
)
# Fetch arguments
tol = parser.parse_args().tol
assert tol > 0.0, "tol <= 0: %s" % tol
maxiter = parser.parse_args().maxiter
assert maxiter > 0, "maxiter <= 0: %s" % maxiter
root = parser.parse_args().root
watch_mode = parser.parse_args().watch_mode
if watch_mode:
watch(root, tol, maxiter)
else:
print_snapshot(root, tol, maxiter)
def watch(root, tol=float("inf"), maxiter=float("inf")):
"""
:param root: Prefix of MultiNest output filenames (root)
:type root: string
:param tol: MultiNest evidence tolerance factor (tol)
:type tol: float
:param maxiter: MultiNest maximum number of iterations (maxiter)
:type maxiter: int
:returns: All information about MultiNest scan
:rtype: dict
"""
# Find folder containing MultiNest output files
folder = dirname(root)
assert isdir(folder), "Cannot find: %s" % folder
live_name = basename(root + "live.points")
# Watch folder containing MultiNest output files
watch_ = inotify.Inotify(block_duration_s=10)
watch_.add_watch(folder)
# Data-holders
time_data = []
ln_delta_data = []
time_start = datetime.now()
print("Start time: %s" % time_start)
for event in watch_.event_gen():
if event and event[3] == live_name:
# Snap MultiNest scan
try:
snap_time = datetime.now()
snap = snapshot(root, tol, maxiter)
except Exception as error:
warn(error.message)
continue
# Record data about delta
time_data.append(snap_time)
ln_delta = [mode["ln_delta"] for mode in snap["modes"].values()]
ln_delta_data.append(ln_delta)
else:
# Make plot of progress
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# ax.set_yscale('symlog')
plt.gcf().autofmt_xdate()
plt.xlabel(r'Time')
plt.ylabel(r'$\ln \Delta \mathcal{Z}$')
# Extrapolate time-remaining and plot, if possible
time_float = [(t - time_start).total_seconds() for t in time_data]
for n_mode, ln_delta in enumerate(map(list, zip(*ln_delta_data))):
fit = np.polyfit(ln_delta, time_float, 10)
time_func = np.poly1d(fit)
time_seconds = time_func(log(tol))
guess_time_end = time_start + timedelta(seconds=time_seconds)
estimate_seconds = [time_func(x) for x in ln_delta]
estimate_time = [time_start + timedelta(seconds=e) for e in estimate_seconds]
plt.plot(estimate_time + [guess_time_end], ln_delta + [log(tol)])
plt.plot(time_data, ln_delta, "*")
plt.axhline(log(tol), color="r")
print("Mode: %s. Estimated end time: %s" %(n_mode, guess_time_end))
plt.savefig("progress.png")
plt.close()
# Quit watching if MultiNest scan has stopped
try:
if snap["global"]["stop"]:
print_snapshot(root, tol, maxiter)
time_end = datetime.now()
delta_time = time_end - time_start
print("End time: %s" % time_end)
print("Total time: %s" % delta_time)
break
except NameError:
continue