weights = np.array([3.0, 3.0, .3, .3, 0.3, 0.3, 3., 3., 0., 3.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([6.0, 0.6, 0.6, 3., 3., 0., 3.])
# XXX-
# This is where we could add a look-ahead basis function to include m5_diff in the future.
# Actually, having a near-future m5 would also help prevent switching to u or g right at twilight?
# Maybe just need a "filter future" basis function?
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))
# Set up the greedy surveys for filling time when can't take pairs.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
greedy_surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(
Target_map_modulo_basis_function(
filtername=filtername,
mod_year=mod_year,
offset=0,
target_map=even_year_target[filtername],
def generate_slair_scheduler():
nside = fs.set_default_nside(nside=32)
# get rid of silly northern strip.
target_map = fs.standard_goals(nside=nside)
norm_factor = fs.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]
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_map[filtername],
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.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.))
weights = np.array([3.0, 3.0, .3, .3, 3., 3., 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.])
# XXX-
# This is where we could add a look-ahead basis function to include m5_diff in the future.
# Actually, having a near-future m5 would also help prevent switching to u or g right at twilight?
# Maybe just need a "filter future" basis function?
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))
pair_surveys.append(surveys[-1])
# Let's set up some standard surveys as well to fill in the gaps. This is my old silly masked version.
# It would be good to put in Tiago's verion and lift nearly all the masking. That way this can also
# chase sucker holes.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
#filters = ['i', 'z', 'y']
greedy_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[filtername],
out_of_bounds_val=hp.UNSEEN,
nside=nside,
norm_factor=norm_factor))
bfs.append(
fs.North_south_patch_basis_function(zenith_min_alt=50.,
nside=nside))
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=60.,
max_alt=76.))
weights = np.array([3.0, 0.3, 1., 3., 3., 0.])
# 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'))
greedy_surveys.append(surveys[-1])
# Set up the DD surveys
dd_surveys = fs.generate_dd_surveys()
surveys.extend(dd_surveys)
survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]
# put in as list-of-lists so pairs get evaluated first.
scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)
return scheduler
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
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])
#filters = ['u', 'g', 'r', 'i', 'z', 'y']
filters = ['y']
greedy_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[filtername],
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=60., max_alt=76.))
weights = np.array([3.0, 3.0, .3, .3, 0.3, 0.3, 3., 3., 0.])
if filtername2 is None:
# Need to scale weights up so filter balancing still works properly.
weights = np.array([6.0, 0.6, 0.6, 3., 3., 0.])
# XXX-
# This is where we could add a look-ahead basis function to include m5_diff in the future.
# Actually, having a near-future m5 would also help prevent switching to u or g right at twilight?
# Maybe just need a "filter future" basis function?
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, ignore_obs='DD'))
# Set up the greedy surveys for filling time when can't take pairs.
filters = ['u', 'g', 'r', 'i', 'z', 'y']
greedy_surveys = []
for filtername in filters:
bfs = []
bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
bfs.append(Target_map_modulo_basis_function(filtername=filtername,
mod_year=mod_year, offset=0,
target_map=even_year_target[filtername],
norm_factor=even_norm))
bfs.append(Target_map_modulo_basis_function(filtername=filtername,
mod_year=mod_year, offset=offset,
target_map=even_year_target[filtername],