def make_rolling_footprints(mjd_start=59853.5,
sun_RA_start=3.27717639,
nslice=2,
scale=0.8):
hp_footprints = standard_goals()
down = 1. - scale
up = nslice - down * (nslice - 1)
start = [1., 1., 1.]
end = [1., 1., 1., 1., 1., 1.]
if nslice == 2:
rolling = [up, down, up, down, up, down]
elif nslice == 3:
rolling = [up, down, down, up, down, down]
elif nslice == 6:
rolling = [up, down, down, down, down, down]
all_slopes = [
start + np.roll(rolling, i).tolist() + end for i in range(nslice)
]
fp_non_wfd = Footprint(mjd_start, sun_RA_start=sun_RA_start)
rolling_footprints = []
for i in range(nslice):
step_func = Step_slopes(rise=all_slopes[i])
rolling_footprints.append(
Footprint(mjd_start,
sun_RA_start=sun_RA_start,
step_func=step_func))
wfd_indx = np.where(hp_footprints['r'] == 1)[0]
non_wfd_indx = np.where(hp_footprints['r'] != 1)[0]
wfd = hp_footprints['r'] * 0
wfd[wfd_indx] = 1
wfd_accum = np.cumsum(wfd)
split_wfd_indices = np.floor(
np.max(wfd_accum) / nslice * (np.arange(nslice) + 1)).astype(int)
split_wfd_indices = split_wfd_indices.tolist()
split_wfd_indices = [0] + split_wfd_indices
for key in hp_footprints:
temp = hp_footprints[key] + 0
temp[wfd_indx] = 0
fp_non_wfd.set_footprint(key, temp)
for i, spi in enumerate(split_wfd_indices[0:-1]):
temp = hp_footprints[key] + 0
temp[non_wfd_indx] = 0
indx = wfd_indx[split_wfd_indices[i]:split_wfd_indices[i + 1]]
temp[indx] = 0
rolling_footprints[i].set_footprint(key, temp)
result = Footprints([fp_non_wfd] + rolling_footprints)
return result
def make_rolling_footprints(mjd_start=59853.5,
sun_RA_start=3.27717639,
nslice=2,
scale=0.8,
nside=32):
hp_footprints = standard_goals(nside=nside)
down = 1. - scale
up = nslice - down * (nslice - 1)
start = [1., 1., 1.]
end = [1., 1., 1., 1., 1., 1.]
if nslice == 2:
rolling = [up, down, up, down, up, down]
elif nslice == 3:
rolling = [up, down, down, up, down, down]
elif nslice == 6:
rolling = [up, down, down, down, down, down]
all_slopes = [
start + np.roll(rolling, i).tolist() + end for i in range(nslice)
]
fp_non_wfd = Footprint(mjd_start, sun_RA_start=sun_RA_start)
rolling_footprints = []
for i in range(nslice):
step_func = Step_slopes(rise=all_slopes[i])
rolling_footprints.append(
Footprint(mjd_start,
sun_RA_start=sun_RA_start,
step_func=step_func))
split_wfd_indices = slice_wfd_area_quad(hp_footprints, nslice=nslice)
wfd = hp_footprints['r'] * 0
wfd_indx = np.where(hp_footprints['r'] == 1)[0]
non_wfd_indx = np.where(hp_footprints['r'] != 1)[0]
wfd[wfd_indx] = 1
roll = np.zeros(nslice)
roll[-1] = 1
for key in hp_footprints:
temp = hp_footprints[key] + 0
temp[wfd_indx] = 0
fp_non_wfd.set_footprint(key, temp)
for i in range(nslice):
temp = hp_footprints[key] + 0
temp[non_wfd_indx] = 0
for j in range(nslice * 2):
indx = wfd_indx[split_wfd_indices[j]:split_wfd_indices[j + 1]]
temp[indx] = temp[indx] * roll[(i + j) % nslice]
rolling_footprints[i].set_footprint(key, temp)
result = Footprints([fp_non_wfd] + rolling_footprints)
return result
extra_info['file executed'] = os.path.realpath(__file__)
fileroot = 'weather_%.1f_' % cloud_limit
file_end = 'v1.6_'
if scale_down:
footprints_hp = nes_light_footprints(nside=nside)
fileroot = fileroot + 'scaleddown_'
else:
footprints_hp = standard_goals(nside=nside)
observatory = Model_observatory(nside=nside)
conditions = observatory.return_conditions()
footprints = Footprint(conditions.mjd_start,
sun_RA_start=conditions.sun_RA_start,
nside=nside)
for i, key in enumerate(footprints_hp):
footprints.footprints[i, :] = footprints_hp[key]
# Set up the DDF surveys to dither
dither_detailer = detailers.Dither_detailer(per_night=per_night,
max_dither=max_dither)
details = [
detailers.Camera_rot_detailer(min_rot=-camera_ddf_rot_limit,
max_rot=camera_ddf_rot_limit),
dither_detailer
]
ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details)
greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints)
def generate_twilight_neo(nside,
night_pattern=None,
nexp=1,
exptime=1,
camera_rot_limits=[-80., 80.],
footprint_weight=0.1,
slewtime_weight=3.,
stayfilter_weight=3.,
mjd_start=0,
sun_RA_start=0.):
# XXX finish eliminating magic numbers and document this one
slew_estimate = 4.5
filters = 'riz'
survey_name = 'twilight_neo'
footprint = ecliptic_target(nside=nside)
footprints_hp = {}
for filtername in filters:
footprints_hp[filtername] = footprint
footprints = Footprint(mjd_start, sun_RA_start=sun_RA_start, nside=nside)
for i, key in enumerate(footprints_hp):
footprints.footprints[i, :] = footprints_hp[key]
surveys = []
for filtername in filters:
detailer_list = []
detailer_list.append(
detailers.Camera_rot_detailer(min_rot=np.min(camera_rot_limits),
max_rot=np.max(camera_rot_limits)))
detailer_list.append(detailers.Close_alt_detailer())
detailer_list.append(
detailers.Twilight_triple_detailer(slew_estimate=slew_estimate,
n_repeat=3))
bfs = []
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))
# 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.), 0))
bfs.append((bf.Zenith_shadow_mask_basis_function(nside=nside,
shadow_minutes=60.,
max_alt=76.), 0))
bfs.append((bf.Moon_avoidance_basis_function(nside=nside,
moon_distance=30.), 0))
bfs.append(
(bf.Filter_loaded_basis_function(filternames=filtername), 0))
bfs.append((bf.Planet_mask_basis_function(nside=nside), 0))
bfs.append((bf.Sun_alt_limit_basis_function(), 0))
bfs.append((bf.Time_in_twilight_basis_function(time_needed=5.), 0))
bfs.append((bf.Night_modulo_basis_function(pattern=night_pattern), 0))
# unpack the basis functions and weights
weights = [val[1] for val in bfs]
basis_functions = [val[0] for val in bfs]
# Set huge ideal pair time and use the detailer to cut down the list of observations to fit twilight?
surveys.append(
Blob_survey(basis_functions,
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