def generate_greedy(nside): target_maps, norm_factor = generate_target_maps(nside) cloud_map = fs.generate_cloud_map(target_maps, filtername='i', wfd_cloud_max=0.7, scp_cloud_max=0.7, gp_cloud_max=0.7, nes_cloud_max=0.7) # filters = ['u', 'g', 'r', 'i', 'z', 'y'] filters = ['g', 'r', 'i', 'z', 'y'] surveys = [] for filtername in filters: bfs = list() bfs.append( fs.M5_diff_basis_function(filtername=filtername, 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)) bfs.append( fs.Aggressive_Slewtime_basis_function(filtername=filtername, nside=nside, order=6., hard_max=120.)) bfs.append(fs.Strict_filter_basis_function(filtername=filtername)) bfs.append( fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=0., max_alt=76.)) bfs.append( fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside)) bfs.append( fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.)) weights = np.array([3., 1., 3., 3., 0., 0, 0]) surveys.append( fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername, dither=True, nside=nside, tag_fields=True, tag_map=target_maps[filtername][1], tag_names=target_maps[filtername][2], ignore_obs='DD')) return surveys
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.Cadence_enhance_basis_function(enhance_window=[2.1, 5.], apply_area=cadence_area, nside=nside)) bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.)) 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])
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
fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.)) bfs.append( Cadence_enhance_basis_function(nside=nside, enhance_window=[2.1, 4.1], apply_area=cadence_area, filtername='gri')) bfs.append( fs.North_south_patch_basis_function(zenith_min_alt=50., zenith_pad=20., nside=nside)) 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., 0., 3., 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., 0., 3., 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))
def year_1_surveys(nside=32, mjd0=None): """ Generate a list of surveys for executing in year 1 """ nside = nside filters = ['u', 'g', 'r', 'i', 'z', 'y'] target_map = large_target_map(nside, dec_max=34.3) norm_factor = fs.calc_norm_factor({'r': target_map}) # set up a cloud map cloud_map = target_map * 0 + 0.7 # Set up map m5-depth limits: m5_limits = {} percentile_cut = 0.7 m52per = sb.M5percentiles() for filtername in filters: m5_limits[filtername] = m52per.percentile2m5map(percentile_cut, filtername=filtername, nside=nside) 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, 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=0., 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 = [3.0, 0.3, 3., 3., 0, 0., 0.] # add in some constriants to make sure we only observe in good conditions and shut off after 3 good ones bfs.append( Limit_m5_map_basis_function(m5_limits[filtername], nside=nside, filtername=filtername)) bfs.append( Seeing_limit_basis_function(nside=nside, filtername=filtername)) bfs.append(Time_limit_basis_function(day_max=365.25)) # XXX--Do I need a m5-depth limit on here too? bfs.append( Nvis_limit_basis_function(nside=nside, filtername=filtername, n_limit=3, seeing_limit=1.2, time_lag=0.45, m5_limit_map=m5_limits[filtername])) weights.extend([0, 0, 0, 0]) #weights.extend([0., 0., 0.]) weights = np.array(weights) # 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', survey_name='templates')) # Do we want to cover all the potential area LSST could observe? In case a GW goes off # in the north not in the NES. return surveys