def generate_greedy(nside):
target_maps, norm_factor = generate_target_maps(nside)
cloud_map = fs.generate_cloud_map(target_maps,
filtername='i',
wfd_cloud_max=0.7,
scp_cloud_max=0.7,
gp_cloud_max=0.7,
nes_cloud_max=0.7)
# filters = ['u', 'g', 'r', 'i', 'z', 'y']
filters = ['g', 'r', 'i', 'z', 'y']
surveys = []
for filtername in filters:
bfs = list()
bfs.append(
fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
fs.Target_map_basis_function(filtername=filtername,
target_map=target_maps[filtername][0],
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
bfs.append(
fs.Aggressive_Slewtime_basis_function(filtername=filtername,
nside=nside,
order=6.,
hard_max=120.))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
bfs.append(
fs.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=0.,
max_alt=76.))
bfs.append(
fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
bfs.append(
fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
weights = np.array([3., 1., 3., 3., 0., 0, 0])
surveys.append(
fs.Greedy_survey_fields(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
tag_fields=True,
tag_map=target_maps[filtername][1],
tag_names=target_maps[filtername][2],
ignore_obs='DD'))
return surveys
out_of_bounds_val=hp.UNSEEN, nside=nside,
norm_factor=norm_factor))
if filtername2 is not None:
bfs.append(fs.Target_map_basis_function(filtername=filtername2,
target_map=target_map[filtername2],
out_of_bounds_val=hp.UNSEEN, nside=nside,
norm_factor=norm_factor))
bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
bfs.append(fs.Cadence_enhance_basis_function(enhance_window=[2.1, 5.], apply_area=cadence_area,
nside=nside))
bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., zenith_pad=20.,
nside=nside))
bfs.append(fs.Quadrant_basis_function(quadrants=['N', 'E', 'S'], azWidth=90.))
bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
weights = np.array([0., 0.3, 0.3, 3., 1., 1., 0., 0., 0., 0., 0.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([0., 0.6, 3., 1., 1., 0., 0., 0., 0., 0.])
if filtername2 is None:
survey_name = 'blob, %s' % filtername
else:
survey_name = 'blob, %s%s' % (filtername, filtername2)
surveys.append(fs.Blob_survey(bfs, weights, filtername=filtername,
filter2=filtername2,
survey_note=survey_name, az_range=180.,
search_radius=90, ignore_obs='DD'))
pair_surveys.append(surveys[-1])
def generate_blob_surveys(nside):
# Define what we want the final visit ratio map to look like
target_maps, norm_factor = generate_target_maps(nside)
# set up a cloud map
cloud_map = target_maps['r'][0] * 0 + 0.7
# Set up observations to be taken in blocks
filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
filter2s = [None, 'g', 'r', 'i', None, None]
pair_surveys = []
for filtername, filtername2 in zip(filter1s, filter2s):
bfs = []
bfs.append(
fs.M5_diff_basis_function(filtername=filtername, nside=nside))
if filtername2 is not None:
bfs.append(
fs.M5_diff_basis_function(filtername=filtername2, nside=nside))
bfs.append(
fs.Target_map_basis_function(filtername=filtername,
target_map=target_maps[filtername][0],
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
if filtername2 is not None:
bfs.append(
fs.Target_map_basis_function(
filtername=filtername2,
target_map=target_maps[filtername2][0],
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
bfs.append(
fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
# Masks, give these 0 weight
bfs.append(
fs.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
bfs.append(
fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(
fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0., 0., 0.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([6.0, 0.6, 3., 3., 0., 0., 0.])
if filtername2 is None:
survey_name = 'blob, %s' % filtername
else:
survey_name = 'blob, %s%s' % (filtername, filtername2)
pair_surveys.append(
fs.Blob_survey(bfs,
weights,
filtername=filtername,
filter2=filtername2,
survey_note=survey_name,
ignore_obs='DD',
tag_fields=True,
tag_map=target_maps[filtername][1],
tag_names=target_maps[filtername][2]))
return pair_surveys
def year_1_surveys(nside=32, mjd0=None):
"""
Generate a list of surveys for executing in year 1
"""
nside = nside
filters = ['u', 'g', 'r', 'i', 'z', 'y']
target_map = large_target_map(nside, dec_max=34.3)
norm_factor = fs.calc_norm_factor({'r': target_map})
# set up a cloud map
cloud_map = target_map * 0 + 0.7
# Set up map m5-depth limits:
m5_limits = {}
percentile_cut = 0.7
m52per = sb.M5percentiles()
for filtername in filters:
m5_limits[filtername] = m52per.percentile2m5map(percentile_cut,
filtername=filtername,
nside=nside)
surveys = []
for filtername in filters:
bfs = []
bfs.append(
fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
fs.Target_map_basis_function(filtername=filtername,
target_map=target_map,
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
bfs.append(
fs.Slewtime_basis_function(filtername=filtername, nside=nside))
bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
bfs.append(
fs.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=0.,
max_alt=76.))
bfs.append(
fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
bfs.append(
fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
weights = [3.0, 0.3, 3., 3., 0, 0., 0.]
# add in some constriants to make sure we only observe in good conditions and shut off after 3 good ones
bfs.append(
Limit_m5_map_basis_function(m5_limits[filtername],
nside=nside,
filtername=filtername))
bfs.append(
Seeing_limit_basis_function(nside=nside, filtername=filtername))
bfs.append(Time_limit_basis_function(day_max=365.25))
# XXX--Do I need a m5-depth limit on here too?
bfs.append(
Nvis_limit_basis_function(nside=nside,
filtername=filtername,
n_limit=3,
seeing_limit=1.2,
time_lag=0.45,
m5_limit_map=m5_limits[filtername]))
weights.extend([0, 0, 0, 0])
#weights.extend([0., 0., 0.])
weights = np.array(weights)
# Might want to try ignoring DD observations here, so the DD area gets covered normally--DONE
surveys.append(
fs.Greedy_survey_fields(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD',
survey_name='templates'))
# Do we want to cover all the potential area LSST could observe? In case a GW goes off
# in the north not in the NES.
return surveys