def run_sched(surveys,
survey_length=365.25,
nside=32,
fileroot='baseline_',
verbose=False,
extra_info=None,
illum_limit=40.):
years = np.round(survey_length / 365.25)
scheduler = Core_scheduler(surveys, nside=nside)
n_visit_limit = None
filter_sched = simple_filter_sched(illum_limit=illum_limit)
observatory = Model_observatory(nside=nside)
conditions_init = copy.deepcopy(observatory.return_conditions())
scheduler.update_conditions(observatory.return_conditions())
desired_obs = scheduler.request_observation(mjd=observatory.mjd)
print(desired_obs)
completed_obs, new_night = observatory.observe(desired_obs)
print(completed_obs)
# Check if there's any difference between where we wanted to point, and where it pointed
for key in ['RA', 'dec', 'rotSkyPos']:
print(key, desired_obs[key] - completed_obs[key], desired_obs[key][0])
conditions_final = observatory.return_conditions()
print(scheduler.survey_lists[2][-2].fields['dec'][3410])
import pdb
pdb.set_trace()
def run_sched(surveys,
survey_length=None,
nside=32,
fileroot='baseline_',
verbose=False,
extra_info=None,
illum_limit=40.,
survey_lengths=None,
pause_lengths=None,
delete_past=True):
years = np.round(np.sum(survey_lengths) / 365.25)
filename = fileroot + '%iyrs.db' % years
scheduler = Core_scheduler(surveys, nside=nside)
n_visit_limit = None
filter_sched = simple_filter_sched(illum_limit=illum_limit)
observatory = Model_observatory(nside=nside)
observations = []
for survey_length, pause_length in zip(survey_lengths, pause_lengths):
observatory, scheduler, observations1 = sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename=None,
delete_past=True,
n_visit_limit=n_visit_limit,
verbose=verbose,
extra_info=extra_info,
filter_scheduler=filter_sched)
observatory.mjd += pause_length
observations.append(observations1)
# Now for the last set of observations
survey_length = survey_lengths[-1]
observatory, scheduler, observations1 = sim_runner(
observatory,
scheduler,
survey_length=survey_length,
filename=None,
delete_past=True,
n_visit_limit=n_visit_limit,
verbose=verbose,
extra_info=extra_info,
filter_scheduler=filter_sched)
observations.append(observations1)
observations = np.concatenate(observations)
info = run_info_table(observatory, extra_info=extra_info)
converter = schema_converter()
print("Writing combined results to %s" % filename)
converter.obs2opsim(observations,
filename=filename,
info=info,
delete_past=delete_past)
def run_sched(surveys, survey_length=365.25, nside=32, fileroot='baseline_', verbose=False,
extra_info=None, illum_limit=40.):
years = np.round(survey_length/365.25)
scheduler = Core_scheduler(surveys, nside=nside)
n_visit_limit = None
filter_sched = simple_filter_sched(illum_limit=illum_limit)
observatory = Model_observatory(nside=nside)
observatory, scheduler, observations = sim_runner(observatory, scheduler,
survey_length=survey_length,
filename=fileroot+'%iyrs.db' % years,
delete_past=True, n_visit_limit=n_visit_limit,
verbose=verbose, extra_info=extra_info,
filter_scheduler=filter_sched)
def run_sched(surveys,
survey_length=None,
nside=32,
fileroot='baseline_',
verbose=False,
extra_info=None,
illum_limit=40.,
sl1=730,
sl2=2922,
pause_length=183,
delete_past=True):
years = np.round((sl1 + sl2 + pause_length) / 365.25)
filename = fileroot + '%iyrs.db' % years
scheduler = Core_scheduler(surveys, nside=nside)
n_visit_limit = None
filter_sched = simple_filter_sched(illum_limit=illum_limit)
observatory = Model_observatory(nside=nside)
observatory, scheduler, observations1 = sim_runner(
observatory,
scheduler,
survey_length=sl1,
filename=None,
delete_past=True,
n_visit_limit=n_visit_limit,
verbose=verbose,
extra_info=extra_info,
filter_scheduler=filter_sched)
observatory.mjd += pause_length
observatory, scheduler, observations2 = sim_runner(
observatory,
scheduler,
survey_length=sl2,
filename=None,
delete_past=True,
n_visit_limit=n_visit_limit,
verbose=verbose,
extra_info=extra_info,
filter_scheduler=filter_sched)
observations = np.concatenate([observations1, observations2])
info = run_info_table(observatory, extra_info=extra_info)
converter = schema_converter()
converter.obs2opsim(observations,
filename=filename,
info=info,
delete_past=delete_past)
def sim_runner(observatory,
scheduler,
filter_scheduler=None,
mjd_start=None,
survey_length=3.,
filename=None,
delete_past=True,
n_visit_limit=None,
step_none=15.,
verbose=True,
extra_info=None,
event_table=None):
"""
run a simulation
Parameters
----------
survey_length : float (3.)
The length of the survey ot run (days)
step_none : float (15)
The amount of time to advance if the scheduler fails to return a target (minutes).
extra_info : dict (None)
If present, dict gets added onto the information from the observatory model.
event_table : np.array (None)
Any ToO events that were included in the simulation
"""
if extra_info is None:
extra_info = {}
t0 = time.time()
if filter_scheduler is None:
filter_scheduler = simple_filter_sched()
if mjd_start is None:
mjd = observatory.mjd + 0
mjd_start = mjd + 0
else:
mjd = mjd_start + 0
observatory.mjd = mjd
end_mjd = mjd + survey_length
observations = []
mjd_track = mjd + 0
step = 1. / 24.
step_none = step_none / 60. / 24. # to days
mjd_run = end_mjd - mjd_start
nskip = 0
new_night = False
mjd_last_flush = -1
while mjd < end_mjd:
if not scheduler._check_queue_mjd_only(observatory.mjd):
scheduler.update_conditions(observatory.return_conditions())
desired_obs = scheduler.request_observation(mjd=observatory.mjd)
if desired_obs is None:
# No observation. Just step into the future and try again.
warnings.warn(
'No observation. Step into the future and trying again.')
observatory.mjd = observatory.mjd + step_none
scheduler.update_conditions(observatory.return_conditions())
nskip += 1
continue
completed_obs, new_night = observatory.observe(desired_obs)
if completed_obs is not None:
scheduler.add_observation(completed_obs[0])
observations.append(completed_obs)
filter_scheduler.add_observation(completed_obs[0])
else:
# An observation failed to execute, usually it was outside the altitude limits.
if observatory.mjd == mjd_last_flush:
raise RuntimeError(
"Scheduler has failed to provide a valid observation multiple times."
)
# if this is a first offence, might just be that targets set. Flush queue and get some new targets.
scheduler.flush_queue()
mjd_last_flush = observatory.mjd + 0
if new_night:
# find out what filters we want mounted
conditions = observatory.return_conditions()
filters_needed = filter_scheduler(conditions)
observatory.observatory.mount_filters(filters_needed)
mjd = observatory.mjd + 0
if verbose:
if (mjd - mjd_track) > step:
progress = float(mjd - mjd_start) / mjd_run * 100
text = "\rprogress = %.2f%%" % progress
sys.stdout.write(text)
sys.stdout.flush()
mjd_track = mjd + 0
if n_visit_limit is not None:
if len(observations) == n_visit_limit:
break
# XXX--handy place to interupt and debug
#if len(observations) > 25:
# import pdb ; pdb.set_trace()
runtime = time.time() - t0
print('Skipped %i observations' % nskip)
print('Flushed %i observations from queue for being stale' %
scheduler.flushed)
print('Completed %i observations' % len(observations))
print('ran in %i min = %.1f hours' % (runtime / 60., runtime / 3600.))
print('Writing results to ', filename)
observations = np.array(observations)[:, 0]
if filename is not None:
info = run_info_table(observatory, extra_info=extra_info)
converter = schema_converter()
converter.obs2opsim(observations,
filename=filename,
info=info,
delete_past=delete_past)
if event_table is not None:
df = pd.DataFrame(event_table)
con = sqlite3.connect(filename)
df.to_sql('events', con)
con.close()
return observatory, scheduler, observations
