def generate_high_am(nside, nexp=1, n_high_am=2, hair_weight=6., pair_time=22.,
camera_rot_limits=[-80., 80.], season=300., season_start_hour=-4., season_end_hour=2.,
shadow_minutes=60., max_alt=76., moon_distance=30., ignore_obs='DD',
m5_weight=6., footprint_weight=0.6, slewtime_weight=3.,
stayfilter_weight=3., template_weight=12., const_weight=1, min_area=288.,
mask_east=True, mask_west=False, survey_name='high_am', filters='ug'):
"""Let's set this up like the blob, but then give it a little extra weight.
"""
target_maps = standard_goals(nside=nside)
surveys = []
for filtername in filters:
survey_name_final = survey_name+', %s' % filtername
target_map = target_maps[filtername]*0
target_map[np.where(target_maps[filtername] == np.max(target_maps[filtername]))] = 1.
detailer_list = []
detailer_list.append(detailers.Spider_rot_detailer())
detailer_list.append(detailers.Close_alt_detailer())
bfs = []
bfs.append((bf.M5_diff_basis_function(filtername=filtername, nside=nside), m5_weight))
bfs.append((bf.Slewtime_basis_function(filtername=filtername, nside=nside), slewtime_weight))
bfs.append((bf.Strict_filter_basis_function(filtername=filtername), slewtime_weight))
bfs.append((bf.N_obs_high_am_basis_function(nside=nside, footprint=target_map, filtername=filtername,
n_obs=n_high_am, season=season,
out_of_bounds_val=np.nan), hair_weight))
bfs.append((bf.Constant_basis_function(), const_weight))
# Masks, give these 0 weight
bfs.append((bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=shadow_minutes, max_alt=max_alt,
penalty=np.nan, site='LSST'), 0.))
bfs.append((bf.Moon_avoidance_basis_function(nside=nside, moon_distance=moon_distance), 0.))
bfs.append((bf.Filter_loaded_basis_function(filternames=filtername), 0))
bfs.append((bf.Time_to_twilight_basis_function(time_needed=pair_time), 0.))
bfs.append((bf.Not_twilight_basis_function(), 0.))
bfs.append((bf.Planet_mask_basis_function(nside=nside), 0.))
if mask_east:
bfs.append((bf.Mask_azimuth_basis_function(az_min=0., az_max=180.), 0.))
if mask_west:
bfs.append((bf.Mask_azimuth_basis_function(az_min=180., az_max=360.), 0.))
weights = [val[1] for val in bfs]
basis_functions = [val[0] for val in bfs]
surveys.append(Blob_survey(basis_functions, weights, filtername1=filtername, filtername2=None,
ideal_pair_time=pair_time, nside=nside,
survey_note=survey_name_final, ignore_obs=ignore_obs, dither=True,
nexp=nexp, detailers=detailer_list, min_area=min_area))
return surveys
def gen_greedy_surveys(nside=32,
nexp=1,
exptime=30.,
filters=['r', 'i', 'z', 'y'],
camera_rot_limits=[-80., 80.],
shadow_minutes=60.,
max_alt=76.,
moon_distance=30.,
ignore_obs='DD',
m5_weight=3.,
footprint_weight=0.3,
slewtime_weight=3.,
stayfilter_weight=3.):
"""
Make a quick set of greedy surveys
This is a convienence function to generate a list of survey objects that can be used with
lsst.sims.featureScheduler.schedulers.Core_scheduler.
To ensure we are robust against changes in the sims_featureScheduler codebase, all kwargs are
explicitly set.
Parameters
----------
nside : int (32)
The HEALpix nside to use
nexp : int (1)
The number of exposures to use in a visit.
exptime : float (30.)
The exposure time to use per visit (seconds)
filters : list of str (['r', 'i', 'z', 'y'])
Which filters to generate surveys for.
camera_rot_limits : list of float ([-80., 80.])
The limits to impose when rotationally dithering the camera (degrees).
shadow_minutes : float (60.)
Used to mask regions around zenith (minutes)
max_alt : float (76.
The maximium altitude to use when masking zenith (degrees)
moon_distance : float (30.)
The mask radius to apply around the moon (degrees)
ignore_obs : str or list of str ('DD')
Ignore observations by surveys that include the given substring(s).
m5_weight : float (3.)
The weight for the 5-sigma depth difference basis function
footprint_weight : float (0.3)
The weight on the survey footprint basis function.
slewtime_weight : float (3.)
The weight on the slewtime basis function
stayfilter_weight : float (3.)
The weight on basis function that tries to stay avoid filter changes.
"""
# Define the extra parameters that are used in the greedy survey. I
# think these are fairly set, so no need to promote to utility func kwargs
greed_survey_params = {
'block_size': 1,
'smoothing_kernel': None,
'seed': 42,
'camera': 'LSST',
'dither': True,
'survey_name': 'greedy'
}
footprints = standard_goals(nside=nside)
sum_footprints = 0
for key in footprints:
sum_footprints += np.sum(footprints[key])
surveys = []
detailer = detailers.Spider_rot_detailer()
for filtername in filters:
bfs = []
bfs.append((bf.M5_diff_basis_function(filtername=filtername,
nside=nside), m5_weight))
bfs.append(
(bf.Footprint_basis_function(filtername=filtername,
footprint=footprints[filtername],
out_of_bounds_val=np.nan,
nside=nside,
all_footprints_sum=sum_footprints),
footprint_weight))
bfs.append((bf.Slewtime_basis_function(filtername=filtername,
nside=nside), slewtime_weight))
bfs.append((bf.Strict_filter_basis_function(filtername=filtername),
stayfilter_weight))
# Masks, give these 0 weight
bfs.append((bf.Zenith_shadow_mask_basis_function(
nside=nside, shadow_minutes=shadow_minutes, max_alt=max_alt), 0))
bfs.append(
(bf.Moon_avoidance_basis_function(nside=nside,
moon_distance=moon_distance), 0))
bfs.append(
(bf.Filter_loaded_basis_function(filternames=filtername), 0))
bfs.append((bf.Planet_mask_basis_function(nside=nside), 0))
weights = [val[1] for val in bfs]
basis_functions = [val[0] for val in bfs]
surveys.append(
Greedy_survey(basis_functions,
weights,
exptime=exptime,
filtername=filtername,
nside=nside,
ignore_obs=ignore_obs,
nexp=nexp,
detailers=[detailer],
**greed_survey_params))
return surveys
def generate_blobs(nside,
nexp=1,
exptime=30.,
filter1s=['u', 'u', 'u', 'g', 'r', 'i', 'z', 'y'],
filter2s=['u', 'g', 'r', 'r', 'i', 'z', 'y', 'y'],
pair_time=22.,
camera_rot_limits=[-80., 80.],
n_obs_template=3,
season=300.,
season_start_hour=-4.,
season_end_hour=2.,
shadow_minutes=60.,
max_alt=76.,
moon_distance=30.,
ignore_obs='DD',
m5_weight=6.,
footprint_weight=0.6,
slewtime_weight=3.,
stayfilter_weight=3.,
template_weight=12.):
"""
Generate surveys that take observations in blobs.
Parameters
----------
nside : int (32)
The HEALpix nside to use
nexp : int (1)
The number of exposures to use in a visit.
exptime : float (30.)
The exposure time to use per visit (seconds)
filter1s : list of str
The filternames for the first set
filter2s : list of str
The filter names for the second in the pair (None if unpaired)
pair_time : float (22)
The ideal time between pairs (minutes)
camera_rot_limits : list of float ([-80., 80.])
The limits to impose when rotationally dithering the camera (degrees).
n_obs_template : int (3)
The number of observations to take every season in each filter
season : float (300)
The length of season (i.e., how long before templates expire) (days)
season_start_hour : float (-4.)
For weighting how strongly a template image needs to be observed (hours)
sesason_end_hour : float (2.)
For weighting how strongly a template image needs to be observed (hours)
shadow_minutes : float (60.)
Used to mask regions around zenith (minutes)
max_alt : float (76.
The maximium altitude to use when masking zenith (degrees)
moon_distance : float (30.)
The mask radius to apply around the moon (degrees)
ignore_obs : str or list of str ('DD')
Ignore observations by surveys that include the given substring(s).
m5_weight : float (3.)
The weight for the 5-sigma depth difference basis function
footprint_weight : float (0.3)
The weight on the survey footprint basis function.
slewtime_weight : float (3.)
The weight on the slewtime basis function
stayfilter_weight : float (3.)
The weight on basis function that tries to stay avoid filter changes.
template_weight : float (12.)
The weight to place on getting image templates every season
"""
blob_survey_params = {
'slew_approx': 7.5,
'filter_change_approx': 140.,
'read_approx': 2.,
'min_pair_time': 15.,
'search_radius': 30.,
'alt_max': 85.,
'az_range': 90.,
'flush_time': 30.,
'smoothing_kernel': None,
'nside': nside,
'seed': 42,
'dither': True,
'twilight_scale': True
}
footprints = standard_goals(nside=nside)
sum_footprints = 0
for key in footprints:
sum_footprints += np.sum(footprints[key])
surveys = []
times_needed = [pair_time, pair_time * 2]
for filtername, filtername2 in zip(filter1s, filter2s):
detailer_list = []
detailer_list.append(detailers.Spider_rot_detailer())
detailer_list.append(detailers.Close_alt_detailer())
# List to hold tuples of (basis_function_object, weight)
bfs = []
if filtername2 is not None:
bfs.append(
(bf.M5_diff_basis_function(filtername=filtername,
nside=nside), m5_weight / 2.))
bfs.append(
(bf.M5_diff_basis_function(filtername=filtername2,
nside=nside), m5_weight / 2.))
else:
bfs.append((bf.M5_diff_basis_function(filtername=filtername,
nside=nside), m5_weight))
if filtername2 is not None:
bfs.append((bf.Footprint_basis_function(
filtername=filtername,
footprint=footprints[filtername],
out_of_bounds_val=np.nan,
nside=nside,
all_footprints_sum=sum_footprints), footprint_weight / 2.))
bfs.append((bf.Footprint_basis_function(
filtername=filtername2,
footprint=footprints[filtername2],
out_of_bounds_val=np.nan,
nside=nside,
all_footprints_sum=sum_footprints), footprint_weight / 2.))
else:
bfs.append((bf.Footprint_basis_function(
filtername=filtername,
footprint=footprints[filtername],
out_of_bounds_val=np.nan,
nside=nside,
all_footprints_sum=sum_footprints), footprint_weight))
bfs.append((bf.Slewtime_basis_function(filtername=filtername,
nside=nside), slewtime_weight))
bfs.append((bf.Strict_filter_basis_function(filtername=filtername),
stayfilter_weight))
if filtername2 is not None:
bfs.append((bf.N_obs_per_year_basis_function(
filtername=filtername,
nside=nside,
footprint=footprints[filtername],
n_obs=n_obs_template,
season=season,
season_start_hour=season_start_hour,
season_end_hour=season_end_hour), template_weight / 2.))
bfs.append((bf.N_obs_per_year_basis_function(
filtername=filtername2,
nside=nside,
footprint=footprints[filtername2],
n_obs=n_obs_template,
season=season,
season_start_hour=season_start_hour,
season_end_hour=season_end_hour), template_weight / 2.))
else:
bfs.append((bf.N_obs_per_year_basis_function(
filtername=filtername,
nside=nside,
footprint=footprints[filtername],
n_obs=n_obs_template,
season=season,
season_start_hour=season_start_hour,
season_end_hour=season_end_hour), template_weight))
# Masks, give these 0 weight
bfs.append((bf.Zenith_shadow_mask_basis_function(
nside=nside,
shadow_minutes=shadow_minutes,
max_alt=max_alt,
penalty=np.nan,
site='LSST'), 0.))
bfs.append(
(bf.Moon_avoidance_basis_function(nside=nside,
moon_distance=moon_distance),
0.))
filternames = [
fn for fn in [filtername, filtername2] if fn is not None
]
bfs.append(
(bf.Filter_loaded_basis_function(filternames=filternames), 0))
if filtername2 is None:
time_needed = times_needed[0]
else:
time_needed = times_needed[1]
bfs.append(
(bf.Time_to_twilight_basis_function(time_needed=time_needed), 0.))
bfs.append((bf.Not_twilight_basis_function(), 0.))
bfs.append((bf.Planet_mask_basis_function(nside=nside), 0.))
# unpack the basis functions and weights
weights = [val[1] for val in bfs]
basis_functions = [val[0] for val in bfs]
if filtername2 is None:
survey_name = 'blob, %s' % filtername
else:
survey_name = 'blob, %s%s' % (filtername, filtername2)
if filtername2 is not None:
detailer_list.append(
detailers.Take_as_pairs_detailer(filtername=filtername2))
surveys.append(
Blob_survey(basis_functions,
weights,
filtername1=filtername,
filtername2=filtername2,
exptime=exptime,
ideal_pair_time=pair_time,
survey_note=survey_name,
ignore_obs=ignore_obs,
nexp=nexp,
detailers=detailer_list,
**blob_survey_params))
return surveys
for arg in sys.argv:
exec_command += ' ' + arg
extra_info['exec command'] = exec_command
try:
extra_info['git hash'] = subprocess.check_output(
['git', 'rev-parse', 'HEAD'])
except subprocess.CalledProcessError:
extra_info['git hash'] = 'Not in git repo'
extra_info['file executed'] = os.path.realpath(__file__)
fileroot = 'spiders_'
file_end = 'v1.4_'
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [detailers.Spider_rot_detailer(), dither_detailer]
ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details)
greedy = gen_greedy_surveys(nside, nexp=nexp)
blobs = generate_blobs(nside, nexp=nexp)
surveys = [ddfs, blobs, greedy]
run_sched(surveys,
survey_length=survey_length,
verbose=verbose,
fileroot=os.path.join(outDir, fileroot + file_end),
extra_info=extra_info,
nside=nside,
illum_limit=illum_limit)