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baselines
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‎2019_runs/baseline/baseline_2019.py

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import numpy as np
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import matplotlib.pylab as plt
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import healpy as hp
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from lsst.sims.featureScheduler.modelObservatory import Model_observatory
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from lsst.sims.featureScheduler.schedulers import Core_scheduler
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from lsst.sims.featureScheduler.utils import standard_goals, calc_norm_factor
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import lsst.sims.featureScheduler.basis_functions as bf
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from lsst.sims.featureScheduler.surveys import (generate_dd_surveys, Greedy_survey,
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Blob_survey, Pairs_survey_scripted)
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from lsst.sims.featureScheduler import sim_runner
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def gen_greedy_surveys(nside):
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"""
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Make a quick set of greedy surveys
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"""
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target_map = standard_goals(nside=nside)
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norm_factor = calc_norm_factor(target_map)
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# Let's remove the bluer filters since this should only be near twilight
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filters = ['r', 'i', 'z', 'y']
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surveys = []
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for filtername in filters:
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bfs = []
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bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Target_map_basis_function(filtername=filtername,
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target_map=target_map[filtername],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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bfs.append(bf.Slewtime_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
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# Masks, give these 0 weight
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bfs.append(bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
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bfs.append(bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
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bfs.append(bf.Clouded_out_basis_function())
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bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
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weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
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surveys.append(Greedy_survey(bfs, weights, block_size=1, filtername=filtername,
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dither=True, nside=nside, ignore_obs='DD'))
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return surveys
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def generate_blobs(nside):
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target_map = standard_goals(nside=nside)
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norm_factor = calc_norm_factor(target_map)
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# List to hold all the surveys (for easy plotting later)
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surveys = []
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# Set up observations to be taken in blocks
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filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
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filter2s = [None, 'g', 'r', 'i', None, None]
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# Ideal time between taking pairs
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pair_time = 22.
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times_needed = [pair_time, pair_time*2]
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for filtername, filtername2 in zip(filter1s, filter2s):
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bfs = []
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bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))
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if filtername2 is not None:
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bfs.append(bf.M5_diff_basis_function(filtername=filtername2, nside=nside))
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bfs.append(bf.Target_map_basis_function(filtername=filtername,
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target_map=target_map[filtername],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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if filtername2 is not None:
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bfs.append(bf.Target_map_basis_function(filtername=filtername2,
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target_map=target_map[filtername2],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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bfs.append(bf.Slewtime_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
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# Masks, give these 0 weight
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bfs.append(bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
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bfs.append(bf.Moon_avoidance_basis_function(nside=nside, moon_distance=30.))
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bfs.append(bf.Clouded_out_basis_function())
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filternames = [fn for fn in [filtername, filtername2] if fn is not None]
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bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))
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if filtername2 is None:
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time_needed = times_needed[0]
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else:
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time_needed = times_needed[1]
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bfs.append(bf.Time_to_twilight_basis_function(time_needed=time_needed))
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bfs.append(bf.Not_twilight_basis_function())
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weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0., 0., 0., 0., 0., 0.])
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if filtername2 is None:
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# Need to scale weights up so filter balancing still works properly.
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weights = np.array([6.0, 0.6, 3., 3., 0., 0., 0., 0., 0., 0.])
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if filtername2 is None:
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survey_name = 'blob, %s' % filtername
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else:
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survey_name = 'blob, %s%s' % (filtername, filtername2)
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surveys.append(Blob_survey(bfs, weights, filtername1=filtername, filtername2=filtername2,
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ideal_pair_time=pair_time, nside=nside,
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survey_note=survey_name, ignore_obs='DD', dither=True))
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return surveys
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if __name__ == "__main__":
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nside = 32
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survey_length = 365.25*10 # Days
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years = int(survey_length/365.25)
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greedy = gen_greedy_surveys(nside)
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ddfs = generate_dd_surveys(nside=nside)
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blobs = generate_blobs(nside)
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surveys = [ddfs, blobs, greedy]
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n_visit_limit = None
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scheduler = Core_scheduler(surveys, nside=nside)
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observatory = Model_observatory(nside=nside)
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observatory, scheduler, observations = sim_runner(observatory, scheduler,
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survey_length=survey_length,
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filename='baseline2019_%i.db' % years,
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n_visit_limit=n_visit_limit)

‎2019_runs/baseline/baseline_colors_2019.py

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import numpy as np
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import matplotlib.pylab as plt
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import healpy as hp
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from lsst.sims.featureScheduler.modelObservatory import Model_observatory
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from lsst.sims.featureScheduler.schedulers import Core_scheduler
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from lsst.sims.featureScheduler.utils import standard_goals, calc_norm_factor
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import lsst.sims.featureScheduler.basis_functions as bf
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from lsst.sims.featureScheduler.surveys import (generate_dd_surveys, Greedy_survey,
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Blob_survey, Pairs_survey_scripted)
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from lsst.sims.featureScheduler import sim_runner
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def gen_greedy_surveys(nside):
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"""
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Make a quick set of greedy surveys
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"""
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target_map = standard_goals(nside=nside)
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norm_factor = calc_norm_factor(target_map)
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# Let's remove the bluer filters since this should only be near twilight
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filters = ['r', 'i', 'z', 'y']
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surveys = []
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for filtername in filters:
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bfs = []
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bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Target_map_basis_function(filtername=filtername,
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target_map=target_map[filtername],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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bfs.append(bf.Slewtime_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
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# Masks, give these 0 weight
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bfs.append(bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
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bfs.append(bf.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
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bfs.append(bf.Clouded_out_basis_function())
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bfs.append(bf.Filter_loaded_basis_function(filternames=filtername))
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weights = np.array([3.0, 0.3, 3., 3., 0., 0., 0., 0.])
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surveys.append(Greedy_survey(bfs, weights, block_size=1, filtername=filtername,
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dither=True, nside=nside, ignore_obs='DD'))
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return surveys
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def generate_blobs(nside):
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target_map = standard_goals(nside=nside)
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norm_factor = calc_norm_factor(target_map)
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# List to hold all the surveys (for easy plotting later)
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surveys = []
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# Set up observations to be taken in blocks
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filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
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filter2s = [None, 'r', 'i', 'z', None, None]
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# Ideal time between taking pairs
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pair_time = 22.
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times_needed = [pair_time, pair_time*2]
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for filtername, filtername2 in zip(filter1s, filter2s):
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bfs = []
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bfs.append(bf.M5_diff_basis_function(filtername=filtername, nside=nside))
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if filtername2 is not None:
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bfs.append(bf.M5_diff_basis_function(filtername=filtername2, nside=nside))
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bfs.append(bf.Target_map_basis_function(filtername=filtername,
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target_map=target_map[filtername],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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if filtername2 is not None:
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bfs.append(bf.Target_map_basis_function(filtername=filtername2,
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target_map=target_map[filtername2],
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out_of_bounds_val=np.nan, nside=nside,
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norm_factor=norm_factor))
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bfs.append(bf.Slewtime_basis_function(filtername=filtername, nside=nside))
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bfs.append(bf.Strict_filter_basis_function(filtername=filtername))
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# Masks, give these 0 weight
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bfs.append(bf.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
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bfs.append(bf.Moon_avoidance_basis_function(nside=nside, moon_distance=30.))
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bfs.append(bf.Clouded_out_basis_function())
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filternames = [fn for fn in [filtername, filtername2] if fn is not None]
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bfs.append(bf.Filter_loaded_basis_function(filternames=filternames))
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if filtername2 is None:
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time_needed = times_needed[0]
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else:
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time_needed = times_needed[1]
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bfs.append(bf.Time_to_twilight_basis_function(time_needed=time_needed))
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bfs.append(bf.Not_twilight_basis_function())
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weights = np.array([3.0, 3.0, .3, .3, 3., 3., 0., 0., 0., 0., 0., 0.])
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if filtername2 is None:
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# Need to scale weights up so filter balancing still works properly.
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weights = np.array([6.0, 0.6, 3., 3., 0., 0., 0., 0., 0., 0.])
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if filtername2 is None:
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survey_name = 'blob, %s' % filtername
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else:
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survey_name = 'blob, %s%s' % (filtername, filtername2)
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surveys.append(Blob_survey(bfs, weights, filtername1=filtername, filtername2=filtername2,
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ideal_pair_time=pair_time, nside=nside,
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survey_note=survey_name, ignore_obs='DD', dither=True))
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return surveys
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if __name__ == "__main__":
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nside = 32
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survey_length = 365.25*10 # Days
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years = int(survey_length/365.25)
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greedy = gen_greedy_surveys(nside)
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ddfs = generate_dd_surveys(nside=nside)
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blobs = generate_blobs(nside)
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surveys = [ddfs, blobs, greedy]
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n_visit_limit = None
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scheduler = Core_scheduler(surveys, nside=nside)
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observatory = Model_observatory(nside=nside)
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observatory, scheduler, observations = sim_runner(observatory, scheduler,
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survey_length=survey_length,
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filename='baseline_colors2019_%i.db' % years,
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n_visit_limit=n_visit_limit)

‎2019_runs/better_roll/debug.ipynb

+33-13
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