def gen_greedy_surveys(nside, nexp=1):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
wfd_halves = wfd_half()
# Let's remove the bluer filters since this should only be near twilight
filters = ['r', 'i', 'z', 'y']
surveys = []
detailer = detailers.Camera_rot_detailer(min_rot=-87., max_rot=87.)
for filtername in filters:
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
bf.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
bf.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
bfs.append(bf.Map_modulo_basis_function(wfd_halves))
# Masks, give these 0 weight
bfs.append(
bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
bfs.append(bf.Planet_mask_basis_function(nside=nside))
weights = np.array([3.0, 0.3, 3., 3., 3., 0., 0., 0., 0.])
surveys.append(
Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD',
nexp=nexp,
detailers=[detailer]))
return surveys
def gen_greedy_surveys(nside,
nexp=1,
m5w=3.,
footprintw=0.3,
sleww=3.,
fcw=3.):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
# Let's remove the bluer filters since this should only be near twilight
filters = ['r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
bf.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
bf.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
# Masks, give these 0 weight
bfs.append(
bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(bf.Clouded_out_basis_function())
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
weights = np.array([m5w, footprintw, sleww, fcw, 0., 0., 0., 0.])
surveys.append(
Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD',
nexp=nexp))
return surveys
def gen_greedy_surveys(nside, add_DD=True):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
filters = ['u', 'g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
bf.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
bf.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
# Masks, give these 0 weight
bfs.append(
bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(bf.Clouded_out_basis_function())
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
surveys.append(
Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD'))
surveys.append(Pairs_survey_scripted(None, ignore_obs='DD'))
if add_DD:
dd_surveys = generate_dd_surveys(nside=nside)
surveys.extend(dd_surveys)
return surveys
def gen_greedy_surveys(nside):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
filters = ['g', 'r', 'i', 'z', 'y']
surveys = []
cloud_map = target_map['r'][0] * 0 + 0.7
for filtername in filters:
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
bf.Target_map_basis_function(filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside))
bfs.append(
bf.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
# Masks, give these 0 weight
bfs.append(
bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(
bf.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
surveys.append(
Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside))
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.,
footprints=None):
"""
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'
}
surveys = []
detailer = detailers.Camera_rot_detailer(min_rot=np.min(camera_rot_limits),
max_rot=np.max(camera_rot_limits))
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,
out_of_bounds_val=np.nan,
nside=nside), 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
