def generate_blobs(nside, mixed_pairs=False, nexp=1, no_pairs=False, m5w=6., footprintw=0.6, sleww=3., fcw=3.): 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'] 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): 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([ m5w / 2., m5w / 2., footprintw / 2., footprintw / 2., sleww, fcw, 0., 0., 0., 0., 0., 0. ]) if filtername2 is None: # Need to scale weights up so filter balancing still works properly. weights = np.array( [m5w, footprintw, sleww, fcw, 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, nexp=nexp)) 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
def gen_greedy_surveys(nside, nexp=1, target_maps=None, mod_year=None, day_offset=None, norm_factor=None, max_season=10): """ Make a quick set of greedy surveys """ target_map = standard_goals(nside=nside) norm_factor = calc_norm_factor(target_map) wfd_halves = wfd_half() # 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)) target_list = [tm[filtername] for tm in target_maps] bfs.append( bf.Target_map_modulo_basis_function(filtername=filtername, target_maps=target_list, season_modulo=mod_year, day_offset=day_offset, out_of_bounds_val=np.nan, nside=nside, norm_factor=norm_factor, max_season=max_season)) bfs.append( bf.Slewtime_basis_function(filtername=filtername, nside=nside)) bfs.append(bf.Strict_filter_basis_function(filtername=filtername)) bfs.append(bf.Map_modulo_basis_function(wfd_halves)) # 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., 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
# Mark position of the sun at the start of the survey. Usefull for rolling cadence. sun_ra_0 = conditions.sunRA # radians offset = create_season_offset(nside, sun_ra_0) max_season = 6 # 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=-87., max_rot=87.), dither_detailer ] ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details) sg = standard_goals() norm_factor = calc_norm_factor(sg) roll_maps = slice_wfd_area_quad(sg, scale_down_factor=scale_down_factor) target_maps = roll_maps + [sg] if Pairs: if mixedPairs: greedy = gen_greedy_surveys(nside, nexp=nexp, target_maps=target_maps, mod_year=mod_year, day_offset=offset, norm_factor=norm_factor, max_season=max_season) blobs = generate_blobs(nside, nexp=nexp, mixed_pairs=True,
import lsst.sims.featureScheduler.surveys as survey import lsst.sims.featureScheduler.basis_functions as basis_functions from lsst.sims.speedObservatory import Speed_observatory import matplotlib.pylab as plt import healpy as hp import time t0 = time.time() survey_length = 365.25 * 1.05 #365.25*10 # days nside = set_default_nside(nside=32) # Define what we want the final visit ratio map to look like years = np.round(survey_length / 365.25) # get rid of silly northern strip. target_map = standard_goals(nside=nside) norm_factor = calc_norm_factor(target_map) # set up a cloud map cloud_map = target_map['r'] * 0 + 0.7 # 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 = [] times_needed = [22., 44.] for filtername, filtername2 in zip(filter1s, filter2s): bfs = []
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