def gen_greedy_surveys(nside, nexp=1): """ 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)) 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.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
def generate_blobs(nside, nexp=1, footprints=None, exptime=30., filter1s=['u', 'u', 'u', 'g', 'r', 'i', 'z', 'y'], filter2s=['u', 'g', 'r', 'r', 'i', 'z', 'y', 'y'], pair_time=22., camera_rot_limits=[-80., 80.], n_obs_template=3, season=300., season_start_hour=-4., season_end_hour=2., shadow_minutes=60., max_alt=76., moon_distance=30., ignore_obs='DD', m5_weight=6., footprint_weight=0.6, slewtime_weight=3., stayfilter_weight=3., template_weight=12., roll_weight=3.): """ Generate surveys that take observations in blobs. Parameters ---------- nside : int (32) The HEALpix nside to use nexp : int (1) The number of exposures to use in a visit. exptime : float (30.) The exposure time to use per visit (seconds) filter1s : list of str The filternames for the first set filter2s : list of str The filter names for the second in the pair (None if unpaired) pair_time : float (22) The ideal time between pairs (minutes) camera_rot_limits : list of float ([-80., 80.]) The limits to impose when rotationally dithering the camera (degrees). n_obs_template : int (3) The number of observations to take every season in each filter season : float (300) The length of season (i.e., how long before templates expire) (days) season_start_hour : float (-4.) For weighting how strongly a template image needs to be observed (hours) sesason_end_hour : float (2.) For weighting how strongly a template image needs to be observed (hours) shadow_minutes : float (60.) Used to mask regions around zenith (minutes) max_alt : float (76. The maximium altitude to use when masking zenith (degrees) moon_distance : float (30.) The mask radius to apply around the moon (degrees) ignore_obs : str or list of str ('DD') Ignore observations by surveys that include the given substring(s). m5_weight : float (3.) The weight for the 5-sigma depth difference basis function footprint_weight : float (0.3) The weight on the survey footprint basis function. slewtime_weight : float (3.) The weight on the slewtime basis function stayfilter_weight : float (3.) The weight on basis function that tries to stay avoid filter changes. template_weight : float (12.) The weight to place on getting image templates every season """ blob_survey_params = { 'slew_approx': 7.5, 'filter_change_approx': 140., 'read_approx': 2., 'min_pair_time': 15., 'search_radius': 30., 'alt_max': 85., 'az_range': 90., 'flush_time': 30., 'smoothing_kernel': None, 'nside': nside, 'seed': 42, 'dither': True, 'twilight_scale': True } sum_footprints = 0 for key in footprints: sum_footprints += np.sum(footprints[key]) surveys = [] wfd_halves = wfd_half() 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=np.min(camera_rot_limits), max_rot=np.max(camera_rot_limits))) detailer_list.append(detailers.Close_alt_detailer()) # List to hold tuples of (basis_function_object, weight) bfs = [] if filtername2 is not None: bfs.append( (bf.M5_diff_basis_function(filtername=filtername, nside=nside), m5_weight / 2.)) bfs.append( (bf.M5_diff_basis_function(filtername=filtername2, nside=nside), m5_weight / 2.)) else: bfs.append((bf.M5_diff_basis_function(filtername=filtername, nside=nside), m5_weight)) if filtername2 is not None: bfs.append((bf.Footprint_basis_function( filtername=filtername, footprint=footprints[filtername], out_of_bounds_val=np.nan, nside=nside, all_footprints_sum=sum_footprints), footprint_weight / 2.)) bfs.append((bf.Footprint_basis_function( filtername=filtername2, footprint=footprints[filtername2], out_of_bounds_val=np.nan, nside=nside, all_footprints_sum=sum_footprints), footprint_weight / 2.)) else: bfs.append((bf.Footprint_basis_function( filtername=filtername, footprint=footprints[filtername], out_of_bounds_val=np.nan, nside=nside, all_footprints_sum=sum_footprints), 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)) if filtername2 is not None: bfs.append((bf.N_obs_per_year_basis_function( filtername=filtername, nside=nside, footprint=footprints[filtername], n_obs=n_obs_template, season=season, season_start_hour=season_start_hour, season_end_hour=season_end_hour), template_weight / 2.)) bfs.append((bf.N_obs_per_year_basis_function( filtername=filtername2, nside=nside, footprint=footprints[filtername2], n_obs=n_obs_template, season=season, season_start_hour=season_start_hour, season_end_hour=season_end_hour), template_weight / 2.)) else: bfs.append((bf.N_obs_per_year_basis_function( filtername=filtername, nside=nside, footprint=footprints[filtername], n_obs=n_obs_template, season=season, season_start_hour=season_start_hour, season_end_hour=season_end_hour), template_weight)) bfs.append((bf.Map_modulo_basis_function(wfd_halves), roll_weight)) # Masks, give these 0 weight bfs.append((bf.Zenith_shadow_mask_basis_function( nside=nside, shadow_minutes=shadow_minutes, max_alt=max_alt, penalty=np.nan, site='LSST'), 0.)) bfs.append( (bf.Moon_avoidance_basis_function(nside=nside, moon_distance=moon_distance), 0.)) filternames = [ fn for fn in [filtername, filtername2] if fn is not None ] bfs.append( (bf.Filter_loaded_basis_function(filternames=filternames), 0)) 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), 0.)) bfs.append((bf.Not_twilight_basis_function(), 0.)) bfs.append((bf.Planet_mask_basis_function(nside=nside), 0.)) # unpack the basis functions and weights weights = [val[1] for val in bfs] basis_functions = [val[0] for val in bfs] 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(basis_functions, weights, filtername1=filtername, filtername2=filtername2, exptime=exptime, ideal_pair_time=pair_time, survey_note=survey_name, ignore_obs=ignore_obs, nexp=nexp, detailers=detailer_list, **blob_survey_params)) return surveys
def gen_greedy_surveys(nside=32, footprints=None, nexp=1, exptime=30., filters=['r', 'i', 'z', 'y'], camera_rot_limits=[-80., 80.], shadow_minutes=60., max_alt=76., moon_distance=30., ignore_obs='DD', m5_weight=3., footprint_weight=0.3, slewtime_weight=3., stayfilter_weight=3., roll_weight=3.): """ Make a quick set of greedy surveys This is a convienence function to generate a list of survey objects that can be used with lsst.sims.featureScheduler.schedulers.Core_scheduler. To ensure we are robust against changes in the sims_featureScheduler codebase, all kwargs are explicitly set. Parameters ---------- nside : int (32) The HEALpix nside to use nexp : int (1) The number of exposures to use in a visit. exptime : float (30.) The exposure time to use per visit (seconds) filters : list of str (['r', 'i', 'z', 'y']) Which filters to generate surveys for. camera_rot_limits : list of float ([-87., 87.]) The limits to impose when rotationally dithering the camera (degrees). shadow_minutes : float (60.) Used to mask regions around zenith (minutes) max_alt : float (76. The maximium altitude to use when masking zenith (degrees) moon_distance : float (30.) The mask radius to apply around the moon (degrees) ignore_obs : str or list of str ('DD') Ignore observations by surveys that include the given substring(s). m5_weight : float (3.) The weight for the 5-sigma depth difference basis function footprint_weight : float (0.3) The weight on the survey footprint basis function. slewtime_weight : float (3.) The weight on the slewtime basis function stayfilter_weight : float (3.) The weight on basis function that tries to stay avoid filter changes. """ # Define the extra parameters that are used in the greedy survey. I # think these are fairly set, so no need to promote to utility func kwargs greed_survey_params = { 'block_size': 1, 'smoothing_kernel': None, 'seed': 42, 'camera': 'LSST', 'dither': True, 'survey_name': 'greedy' } sum_footprints = 0 for key in footprints: sum_footprints += np.sum(footprints[key]) surveys = [] detailer = detailers.Camera_rot_detailer(min_rot=np.min(camera_rot_limits), max_rot=np.max(camera_rot_limits)) wfd_halves = wfd_half() for filtername in filters: bfs = [] bfs.append((bf.M5_diff_basis_function(filtername=filtername, nside=nside), m5_weight)) bfs.append( (bf.Footprint_basis_function(filtername=filtername, footprint=footprints[filtername], out_of_bounds_val=np.nan, nside=nside, all_footprints_sum=sum_footprints), 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)) bfs.append((bf.Map_modulo_basis_function(wfd_halves), roll_weight)) # Masks, give these 0 weight bfs.append((bf.Zenith_shadow_mask_basis_function( nside=nside, shadow_minutes=shadow_minutes, max_alt=max_alt), 0)) bfs.append( (bf.Moon_avoidance_basis_function(nside=nside, moon_distance=moon_distance), 0)) bfs.append( (bf.Filter_loaded_basis_function(filternames=filtername), 0)) bfs.append((bf.Planet_mask_basis_function(nside=nside), 0)) weights = [val[1] for val in bfs] basis_functions = [val[0] for val in bfs] surveys.append( Greedy_survey(basis_functions, weights, exptime=exptime, filtername=filtername, nside=nside, ignore_obs=ignore_obs, nexp=nexp, detailers=[detailer], **greed_survey_params)) return surveys
def generate_blobs(nside, mixed_pairs=False, nexp=1, no_pairs=False, offset=None, template_weight=0.6, target_maps=None, norm_factor=None, mod_year=2, max_season=10, day_offset=None, footprint_weight=6.): target_map = standard_goals(nside=nside) norm_factor = calc_norm_factor(target_map) wfd_halves = wfd_half() # 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)) 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)) if filtername2 is not None: target_list = [tm[filtername2] for tm in target_maps] bfs.append(bf.Target_map_modulo_basis_function(filtername=filtername2, 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.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.)) 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=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., 3., footprint_weight/2., footprint_weight/2., 3., 3., template_weight, template_weight, 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., footprint_weight, 3., 3., template_weight*2, 3., 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