max_dither = args.maxDither nside = 32 extra_info = {} exec_command = '' for arg in sys.argv: exec_command += ' ' + arg extra_info['exec command'] = exec_command extra_info['git hash'] = subprocess.check_output( ['git', 'rev-parse', 'HEAD']) extra_info['file executed'] = os.path.realpath(__file__) dither_detailer = detailers.Dither_detailer(per_night=per_night, max_dither=max_dither) detailers = [detailers.Zero_rot_detailer(nside=nside), dither_detailer] old_ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=detailers) desc_ddfs = generate_desc_dd_surveys(nside=nside, nexp=nexp, detailers=detailers) # take the old u-band ddfs and use those for survey in old_ddfs: if 'DD:u' in survey.survey_name: desc_ddfs.append(survey) ddfs = desc_ddfs name = 'desc_ddf_' if per_night: name += 'pn_' name += '%.2fdeg_' % max_dither
def generate_blobs(nside, mixed_pairs=False, nexp=1, no_pairs=False, nshort=2, short_time=1.): target_map = standard_goals(nside=nside) norm_factor = calc_norm_factor(target_map) short_footprints = {} for key in target_map: short_footprints[key] = target_map[key] / target_map[key] # 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([3.0, 3.0, .3, .3, 3., 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([6.0, 0.6, 3., 3., 0., 0., 0., 0., 0., 0.]) if filtername2 is None: survey_name = 'blob, %s' % filtername else: survey_name = 'blob, %s%s' % (filtername, filtername2) deets = [] # Need to make sure I have the rotation angle in there deets.append(detailers.Zero_rot_detailer(nside=nside)) deets.append( detailers.Short_expt_detailer( filtername=filtername, nside=nside, nobs=nshort, exp_time=short_time, footprint=short_footprints[filtername])) if (filtername2 is not None) & (filtername2 != filtername): deets.append( detailers.Short_expt_detailer( filtername=filtername2, nside=nside, nobs=nshort, exp_time=short_time, footprint=short_footprints[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=deets)) return surveys
def generate_blobs(nside, nexp=1, exptime=30., filter1s=['u', 'g', 'r', 'i', 'z', 'y'], filter2s=[None, 'g', 'r', 'i', 'z', None], pair_time=22., camera_rot_limits=[0., 0.], 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., footprints=None): """ 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 } surveys = [] times_needed = [pair_time, pair_time * 2] for filtername, filtername2 in zip(filter1s, filter2s): detailer_list = [] detailer_list.append(detailers.Zero_rot_detailer()) 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, out_of_bounds_val=np.nan, nside=nside), footprint_weight / 2.)) bfs.append((bf.Footprint_basis_function(filtername=filtername2, footprint=footprints, out_of_bounds_val=np.nan, nside=nside), footprint_weight / 2.)) else: 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)) if filtername2 is not None: bfs.append((bf.N_obs_per_year_basis_function( filtername=filtername, nside=nside, footprint=footprints.get_footprint(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.get_footprint(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.get_footprint(filtername), n_obs=n_obs_template, season=season, season_start_hour=season_start_hour, season_end_hour=season_end_hour), template_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, nexp=1, exptime=30., filters=['r', 'i', 'z', 'y'], camera_rot_limits=[0., 0.], 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., footprints=None): """ 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 ([-80., 80.]) 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' } surveys = [] detailer = detailers.Zero_rot_detailer() 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, 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)) # 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
fileroot += 'nexp%i_' % nexp file_end = 'v1.6_' footprints_hp = simple_footprint() 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.Zero_rot_detailer(), dither_detailer] ddfs = generate_dd_surveys(nside=nside, nexp=nexp, detailers=details, frac_total=0.0045, aggressive_frac=0.002) greedy = gen_greedy_surveys(nside, nexp=nexp, footprints=footprints) blobs = generate_blobs(nside, nexp=nexp, footprints=footprints) surveys = [ddfs, blobs, greedy] run_sched(surveys, survey_length=survey_length, verbose=verbose, fileroot=os.path.join(outDir, fileroot + file_end), extra_info=extra_info, nside=nside,