def generate_blobs(nside):
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
# List to hold all the surveys (for easy plotting later)
surveys = []
# Set up observations to be taken in blocks
filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
filter2s = [None, 'g', 'r', 'i', None, None]
# Ideal time between taking pairs
pair_time = 22.
times_needed = [pair_time, pair_time*2]
for filtername, filtername2 in zip(filter1s, filter2s):
bfs = []
bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))
if filtername2 is not None:
bfs.append(bf.M5_diff_basis_function(filtername=filtername2, 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))
if filtername2 is not None:
bfs.append(bf.Target_map_basis_function(filtername=filtername2,
target_map=target_map[filtername2],
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=30.))
bfs.append(bf.Clouded_out_basis_function())
filternames = [fn for fn in [filtername, filtername2] if fn is not None]
bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))
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))
bfs.append(bf.Not_twilight_basis_function())
weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0., 0., 0., 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., 0., 0., 0.])
if filtername2 is None:
survey_name = 'blob, %s' % filtername
else:
survey_name = 'blob, %s%s' % (filtername, filtername2)
surveys.append(Blob_survey(bfs, weights, filtername1=filtername, filtername2=filtername2,
ideal_pair_time=pair_time, nside=nside,
survey_note=survey_name, ignore_obs='DD', dither=True))
return surveys
def gen_greedy_surveys(nside, nexp=1):
"""
Make a quick set of greedy surveys
"""
target_map = u_heavy_footprint(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 = []
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))
# 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., 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 generate_third(nside, nexp=1, min_area=100.):
"""A survey to take a third observation in a night
"""
# OK, I want to see if there have been a successful pair taken in the night
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
surveys = []
to_pair = ['gi', 'rz']
for pairs in to_pair:
for filtername in pairs:
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[pairs[0]],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
bf.Third_observation_basis_function(filtername1=pairs[0],
filtername2=pairs[1],
gap_min=40.,
gap_max=120.))
# 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=30.))
bfs.append(bf.Clouded_out_basis_function())
filternames = [fn for fn in pairs]
bfs.append(
bf.Filter_loaded_basis_function(filternames=filternames))
bfs.append(bf.Not_twilight_basis_function())
weights = [1., 1., 1., 0, 0, 0, 0, 0]
survey = Blob_survey(bfs,
weights,
filtername1=pairs[0],
filtername2=None,
ignore_obs='DD',
nexp=nexp,
nside=nside,
min_area=min_area,
survey_note='third, %s' % pairs[0])
surveys.append(survey)
return surveys
def testsched(self):
target_map = standard_goals()['r']
bfs = []
bfs.append(basis_functions.M5_diff_basis_function())
bfs.append(
basis_functions.Target_map_basis_function(target_map=target_map))
weights = np.array([1., 1])
survey = surveys.Greedy_survey(bfs, weights)
scheduler = Core_scheduler([survey])
observatory = Model_observatory()
# Check that we can update conditions
scheduler.update_conditions(observatory.return_conditions())
# Check that we can get an observation out
obs = scheduler.request_observation()
assert (obs is not None)
# Check that we can flush the Queue
scheduler.flush_queue()
assert (len(scheduler.queue) == 0)
# Check that we can add an observation
scheduler.add_observation(obs)
def gen_greedy_surveys(nside, nexp=1):
"""
Make a quick set of greedy surveys
"""
target_map = altfootprint(nside=nside)
norm_factor = calc_norm_factor(target_map)
# Let's remove the bluer filters since this should only be near twilight
filters = ['y']
surveys = []
dec_limits = [[-24, 10], [-90, -20]]
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.Dec_modulo_basis_function(nside=nside, dec_limits=dec_limits))
# 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., 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))
return surveys
def blob_comcam(nexp=1, nside=256, filters=['g', 'r', 'i']):
target_map = standard_goals(nside=nside)
ra, dec = ra_dec_hp_map(nside=nside)
# out_region = np.where((dec > np.radians(-40)) | (dec < np.radians(-50.)))
in_region = np.where((dec <= np.radians(-40.)) & (dec >= np.radians(-50.)))
for key in target_map:
target_map[key] *= 0.
target_map[key][in_region] = 1.
final_tm = {}
for key in filters:
final_tm[key] = target_map[key]
target_map = final_tm
norm_factor = calc_norm_factor(target_map)
survey_list = []
time_needed = 23.
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.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))
bfs.append(bf.Time_to_twilight_basis_function(time_needed=time_needed))
bfs.append(bf.Not_twilight_basis_function())
weights = np.array([3.0, 0.3, 6., 3., 0., 0., 0., 0, 0, 0])
# XXX-Note, need a new detailer here!, have to have dither=False until that can get passed through
sv = surveys.Blob_survey(
bfs,
weights,
filtername1=filtername,
filtername2=None,
dither=False,
nside=nside,
ignore_obs='DD',
nexp=nexp,
camera='comcam',
detailers=[fs.detailers.Comcam_90rot_detailer()])
survey_list.append(sv)
return survey_list
def gen_greedy_surveys(nside, nexp=1):
"""
Make a quick set of greedy surveys
"""
target_map = standard_goals(nside=nside)
# Let's just set it to 1 in all filters
for key in target_map:
target_map[key][np.where(target_map[key] > 0)] = 1
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', 'blob'],
nexp=nexp,
exptime=1.,
survey_name='twilight'))
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 generate_greedy_sched(nexp=1, nside=32, filters=['r']):
# Generate a target map
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
greedy_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))
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.])
sv = surveys.Greedy_survey(bfs,
weights,
block_size=1,
filtername=filtername,
dither=True,
nside=nside,
ignore_obs='DD',
nexp=nexp)
greedy_surveys.append(sv)
survey_list = greedy_surveys
return survey_list
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 generate_blobs(nside,
mixed_pairs=False,
nexp=1,
no_pairs=False,
offset=None,
template_weight=6.):
target_map = wfd_only_fp(nside=nside)
norm_factor = calc_norm_factor(target_map)
# List to hold all the surveys (for easy plotting later)
surveys = []
# Set up observations to be taken in blocks
filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
if mixed_pairs:
filter2s = [None, 'r', 'i', 'z', None, None]
else:
filter2s = [None, 'g', 'r', 'i', None, None]
if no_pairs:
filter2s = [None, None, None, None, None, None]
# Ideal time between taking pairs
pair_time = 22.
times_needed = [pair_time, pair_time * 2]
for filtername, filtername2 in zip(filter1s, filter2s):
detailer_list = []
detailer_list.append(
detailers.Camera_rot_detailer(min_rot=-87., max_rot=87.))
detailer_list.append(detailers.Close_alt_detailer())
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
if filtername2 is not None:
bfs.append(
bf.M5_diff_basis_function(filtername=filtername2, 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))
if filtername2 is not None:
bfs.append(
bf.Target_map_basis_function(
filtername=filtername2,
target_map=target_map[filtername2],
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.N_obs_per_year_basis_function(filtername=filtername,
nside=nside,
footprint=target_map[filtername],
n_obs=3,
season=300.))
if filtername2 is not None:
bfs.append(
bf.N_obs_per_year_basis_function(
filtername=filtername2,
nside=nside,
footprint=target_map[filtername2],
n_obs=3,
season=300.))
# 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=30.))
filternames = [
fn for fn in [filtername, filtername2] if fn is not None
]
bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))
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))
bfs.append(bf.Not_twilight_basis_function())
bfs.append(bf.Planet_mask_basis_function(nside=nside))
weights = np.array([
3.0, 3.0, .3, .3, 3., 3., template_weight, template_weight, 0., 0.,
0., 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., template_weight * 2, 0., 0., 0., 0., 0., 0.
])
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(bfs,
weights,
filtername1=filtername,
filtername2=filtername2,
ideal_pair_time=pair_time,
nside=nside,
survey_note=survey_name,
ignore_obs='DD',
dither=True,
nexp=nexp,
detailers=detailer_list))
return surveys
pair_surveys = []
times_needed = [22., 44.]
for filtername, filtername2 in zip(filter1s, filter2s):
bfs = []
bfs.append(
basis_functions.M5_diff_basis_function(filtername=filtername,
nside=nside))
if filtername2 is not None:
bfs.append(
basis_functions.M5_diff_basis_function(filtername=filtername2,
nside=nside))
bfs.append(
basis_functions.Target_map_basis_function(
filtername=filtername,
target_map=target_map[filtername],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
if filtername2 is not None:
bfs.append(
basis_functions.Target_map_basis_function(
filtername=filtername2,
target_map=target_map[filtername2],
out_of_bounds_val=np.nan,
nside=nside,
norm_factor=norm_factor))
bfs.append(
basis_functions.Slewtime_basis_function(filtername=filtername,
nside=nside))
bfs.append(
basis_functions.Strict_filter_basis_function(filtername=filtername))
def generate_twilight_neo(nside, night_pattern=None):
surveys = []
nexp = 1
filters = 'riz'
survey_name = 'twilight_neo'
target_map_one = ecliptic_target(nside=nside)
target_map = {}
for filtername in filters:
target_map[filtername] = target_map_one
norm_factor = calc_norm_factor(target_map)
exptime = 1.
for filtername in filters:
detailer_list = []
detailer_list.append(
detailers.Camera_rot_detailer(min_rot=-87., max_rot=87.))
detailer_list.append(detailers.Close_alt_detailer())
detailer_list.append(
detailers.Twilight_triple_detailer(slew_estimate=4.5, n_repeat=3))
bfs = []
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))
# XXX
# Need a toward the sun, reward high airmass, with an airmass cutoff basis function.
bfs.append(
bf.Near_sun_twilight_basis_function(nside=nside, max_airmass=2.))
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=30.))
bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
bfs.append(bf.Planet_mask_basis_function(nside=nside))
bfs.append(bf.Sun_alt_limit_basis_function())
bfs.append(bf.Time_in_twilight_basis_function(time_needed=5.))
bfs.append(bf.Night_modulo_basis_function(pattern=night_pattern))
weights = [0.1, 3., 3., 3., 0., 0., 0., 0., 0., 0., 0.]
# Set huge ideal pair time and use the detailer to cut down the list of observations to fit twilight?
surveys.append(
Blob_survey(bfs,
weights,
filtername1=filtername,
filtername2=None,
ideal_pair_time=3.,
nside=nside,
exptime=exptime,
survey_note=survey_name,
ignore_obs=['DD', 'greedy', 'blob'],
dither=True,
nexp=nexp,
detailers=detailer_list,
az_range=180.,
twilight_scale=False))
return surveys
def gen_blob_surveys(nside):
"""
make a quick set of blob surveys
"""
target_map = standard_goals(nside=nside)
norm_factor = calc_norm_factor(target_map)
filter1s = ['u', 'g'] # , 'r', 'i', 'z', 'y']
filter2s = [None, 'g'] # , 'r', 'i', None, None]
filter1s = ['g'] # , 'r', 'i', 'z', 'y']
filter2s = ['g'] # , 'r', 'i', None, None]
pair_surveys = []
for filtername, filtername2 in zip(filter1s, filter2s):
detailer_list = []
bfs = []
bfs.append(
bf.M5_diff_basis_function(filtername=filtername, nside=nside))
if filtername2 is not None:
bfs.append(
bf.M5_diff_basis_function(filtername=filtername2, 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))
if filtername2 is not None:
bfs.append(
bf.Target_map_basis_function(
filtername=filtername2,
target_map=target_map[filtername2],
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=30.))
bfs.append(bf.Clouded_out_basis_function())
# feasibility basis fucntions. Also give zero weight.
filternames = [
fn for fn in [filtername, filtername2] if fn is not None
]
bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))
bfs.append(bf.Time_to_twilight_basis_function(time_needed=22.))
bfs.append(bf.Not_twilight_basis_function())
bfs.append(bf.Planet_mask_basis_function(nside=nside))
weights = np.array(
[3.0, 3.0, .3, .3, 3., 3., 0., 0., 0., 0., 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., 0., 0., 0., 0.])
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))
pair_surveys.append(
Blob_survey(bfs,
weights,
filtername1=filtername,
filtername2=filtername2,
survey_note=survey_name,
ignore_obs='DD',
detailers=detailer_list))
return pair_surveys
