def process_sim(isim, args):
"""Process one simulation."""
if args.have_full_sim_dir:
args.wdir = isim
else:
args.wdir = xl.get_sim_dir(args.base_dir, isim)
args.dmo_dir = xl.get_sim_dir(args.base_dir, args.dmo_sim)
print(f"Matching simulation {args.wdir} to DMO sim {args.dmo_dir}...")
for isnap in args.snapshots:
process_snapshot(isim, isnap, args)
def main():
"""Main loop over simulations and snapshots."""
if args.sims[0].lower() == 'all':
args.sims = xl.get_all_sims(args.base_dir)
have_full_sim_dir = True
else:
have_full_sim_dir = False
for isim in args.sims:
if have_full_sim_dir:
wdir = isim
else:
wdir = xl.get_sim_dir(args.base_dir, isim)
print("")
print(
"===================================================================="
)
print(f"=== Processing {wdir} ===")
print(
"===================================================================="
)
print("")
for iisnap, isnap in enumerate(args.snaps):
# Account for possibly different VR numbering than (desired) output
if args.vr_snaps is None:
ivsnap = isnap
else:
ivsnap = args.vr_snaps[iisnap]
process_snap(wdir, args.out_file, isnap, ivsnap)
def process_sim(args, isim, have_full_sim_dir):
"""Process one individual simulation."""
if have_full_sim_dir:
wdir = isim
else:
wdir = xl.get_sim_dir(args.base_dir, isim)
print(f"Processing simulation {wdir}...")
# Check if output directory exists, create if needed
if not os.path.isdir(os.path.dirname(f'{wdir}{args.link_names}')):
os.makedirs(os.path.dirname(f'{wdir}{args.link_names}'))
# Load output codes from ASCII file
if not os.path.isfile(args.list_file):
print(f"It looks like the specified file '{args.list_file}' "
f"does not exist...")
set_trace()
output_table = ascii.read(args.list_file, format='no_header')
f = open(args.list_file, "r")
line1 = f.readline()
f.close()
if line1.startswith('# Redshift'):
print("Output file in redshift units")
else:
set_trace()
zred = np.array(output_table['col1'])
types = np.array(output_table['col2'])
ind_snaps = np.nonzero(types == args.snapshot_type)[0]
print(f"Found {len(ind_snaps)} snapshots (type {args.snapshot_type})...")
for iisnap, isnap in enumerate(ind_snaps):
create_link(iisnap, isnap, wdir, args)
print(" ...done!")
def main():
print("Parsing input arguments...")
parser = argparse.ArgumentParser(description="Parse input parameters.")
parser.add_argument('sim', help='Simulation index or name to analyse')
parser.add_argument('--base_dir',
help=f'Simulation base directory, default: '
f'{local.BASE_DIR}',
default=local.BASE_DIR)
parser.add_argument('--bh_mmax',
type=float,
help='Maximum BH mass, for the scaling in the images '
'in log (M/M_Sun), default: 8.5.',
default=8.5)
parser.add_argument('--numpix',
type=int,
help='Size of images in pixels, default: 1000',
default=1000)
parser.add_argument('--gallery_dir', default='gallery')
parser.add_argument('--plot_prefix',
default='vr_plots',
help='Prefix for plot files (within gallery_dir), '
'default: vr_plots')
parser.add_argument('--snapshots',
nargs='+',
type=int,
help='Snapshots for which to set up websites.')
parser.add_argument('--snap_name',
default='snapshot',
help='Snapshot prefix, default: "snapshot".')
parser.add_argument('--mstar_min',
type=float,
help='Minimum stellar mass of the host galaxy for '
'a black hole to be included, in M_Sun '
'(default: 3e10)',
default=3e10)
parser.add_argument('--m200_min',
type=float,
help='Minimum M200c of the host galaxy for '
'a black hole to be included, in M_Sun '
'(default: 0, i.e. select on stellar mass only)',
default=0.0)
parser.add_argument('--bh_data_file',
default='black_hole_data.hdf5',
help='Name of the file containing the BH data, '
'default: "black_hole_data.hdf5"')
parser.add_argument('--vr_prefix',
default='vr',
help='Prefix of (combined) VR catalogue, default: vr.')
parser.add_argument('--snap_frontpage',
type=int,
help='Snapshot for images on frontpage (default: 36)',
default=36)
parser.add_argument('--size_frontpage',
type=float,
help='Size of image for front page, in pMpc '
'(default: 0.03)',
default=0.03)
args = parser.parse_args()
if len(args.snapshots) == 0:
print("No snapshots selected, aborting.")
# Adjust selected front page size to closest match in list
frontpage_sizeind = np.argmin(
np.abs(np.array(ap_list) - args.size_frontpage))
args.size_frontpage = ap_list[frontpage_sizeind]
# Construct the full working directory of the simulation
args.wdir = xl.get_sim_dir(args.base_dir, args.sim)
# Look up the snapshot redshifts
get_snapshot_redshifts(args)
args.plotdata_file = (f'{args.wdir}{args.gallery_dir}/'
f'{args.plot_prefix}.hdf5')
if os.path.isfile(args.plotdata_file):
bh_list = hd.read_data(args.plotdata_file, 'BlackHoleBIDs')
select_list = None
print(f"Using pre-created BH list with {len(bh_list)} BHs.")
else:
# Find BHs we are intereste in, load data
select_list = [["Halo_M200c", '>=', args.m200_min],
["Halo_MStar", '>=', args.mstar_min],
["Flag_MostMassiveInHalo", '==', 1],
["HaloTypes", '==', 10]]
bh_list = None
bh_data, bh_sel = xl.lookup_bh_data(args.wdir + args.bh_data_file,
bh_props_list, select_list)
if bh_list is None:
bh_list = bh_sel
if len(bh_list) == 0:
print("No black holes selected, aborting.")
return
# Generate the script to auto-generate all the required images
generate_image_script(args, bh_list)
# Generate the script to auto-generate all the tracks
generate_track_script(args, bh_list)
generate_website(args, bh_data, bh_list)
def process_sim(args, isim, have_full_sim_dir):
"""Generate the images for one particular simulation."""
if have_full_sim_dir:
args.wdir = isim
else:
args.wdir = xl.get_sim_dir(args.base_dir, isim)
# Name of the input BH data catalogue
args.catloc = f'{args.wdir}{args.bh_file}'
# Find BHs we are intereste in, load data (of internal VR match)
select_list = [["Halo_MStar", '>=', args.halo_mstar_range[0]],
["Halo_MStar", '<', args.halo_mstar_range[1]],
["DMO_M200c", '>=', args.halo_m200_range[0]],
["DMO_M200c", '<', args.halo_m200_range[1]]]
if not args.include_subdominant_bhs:
select_list.append(['Flag_MostMassiveInHalo', '==', 1])
if not args.include_satellites:
select_list.append(['HaloTypes', '==', 10])
if args.bh_mass_range is not None:
zreds = hd.read_data(args.wdir + args.bh_file, 'Redshifts')
best_index = np.argmin(np.abs(zreds - args.bh_selection_redshift))
print(f"Best index for redshift {args.bh_selection_redshift} is "
f"{best_index}.")
# Subgrid masses are in 10^10 M_sun, so need to adjust selection range
select_list.append(
['SubgridMasses', '>=', args.bh_mass_range[0] / 1e10, best_index])
select_list.append(
['SubgridMasses', '<=', args.bh_mass_range[1] / 1e10, best_index])
bh_props_list = ['SubgridMasses', 'Redshifts', 'ParticleIDs']
bh_data, bh_list = xl.lookup_bh_data(args.wdir + args.bh_file,
bh_props_list, select_list)
if len(bh_list) == 0:
print("No BHs selected, aborting.")
return
args.sim_pointdata_loc = args.wdir + args.plot_prefix + '.hdf5'
if not os.path.isdir(os.path.dirname(args.sim_pointdata_loc)):
os.makedirs(os.path.dirname(args.sim_pointdata_loc))
if os.path.isfile(args.sim_pointdata_loc):
shutil.move(args.sim_pointdata_loc, args.sim_pointdata_loc + '.old')
hd.write_data(args.sim_pointdata_loc,
'BlackHoleBIDs',
bh_list,
comment='BIDs of all BHs selected for this simulation.')
# Go through snapshots
for iisnap, isnap in enumerate(args.snapshots):
print("")
print(f"Processing snapshot {isnap}...")
# Need to explicitly connect BHs to VR catalogue from this snap
vr_data = xl.connect_to_galaxies(
bh_data['ParticleIDs'][bh_list],
f'{args.wdir}{args.vr_file}_{isnap:04d}',
extra_props=[('ApertureMeasurements/Projection1/30kpc/'
'Stars/HalfMassRadii', 'StellarHalfMassRad'),
('MatchInDMO/M200crit', 'DMO_M200c')])
# Add subgrid mass of BHs themselves
ind_bh_cat = np.argmin(
np.abs(bh_data['Redshifts'] - vr_data['Redshift']))
print(f"Using BH masses from index {ind_bh_cat}")
vr_data['BH_SubgridMasses'] = (
bh_data['SubgridMasses'][bh_list, ind_bh_cat] * 1e10)
n_good_m200 = np.count_nonzero(vr_data['DMO_M200c'] * 0 == 0)
print(f"Have {n_good_m200} BHs with DMO_M200c, out of "
f"{len(bh_list)}.")
# Make plots for each BH, and "general" overview plot
print("Overview plot")
generate_vr_plots(args, vr_data, isnap)
if not args.summary_only:
for iibh, ibh in enumerate(bh_list):
print(f"Make plot for BH-BID {ibh} ({iibh}/{len(bh_list)})")
generate_vr_plots(args, vr_data, isnap, iibh, ibh)
print("Done!")
def process_sim(args, isim, have_full_sim_dir):
args.first_snap = None
args.last_snap = None
if have_full_sim_dir:
args.wdir = isim
else:
args.wdir = xl.get_sim_dir(args.base_dir, isim)
if args.out_dir is None:
args.out_dir = args.wdir
# Find total number of black holes and assign their black-IDs
bpart_ids, bpart_first_output = find_black_ids(args)
if args.first_snap is None:
print("Did not find any black holes, aborting.")
return
# Set up output arrays
output_dict, comment_dict = setup_output(args)
# For efficiency, create a reverse list of BH IDs (if possible)
if max(bpart_ids) < 1e10:
bpart_rev_ids = hx.ReverseList(bpart_ids, assume_positive=True)
use_rev_list = True
else:
use_rev_list = False
# Retrieve header info from first valid snapshot
collect_header_data(args)
# Loop through all snapshots and fill output arrays
for iisnap, isnap in enumerate(range(args.first_snap, args.last_snap + 1)):
if use_rev_list:
process_output(iisnap,
isnap,
output_dict,
bpart_ids,
args,
bpart_rev_ids=bpart_rev_ids)
else:
process_output(iisnap,
isnap,
output_dict,
bpart_ids,
args,
bpart_rev_ids=None)
# Connect black holes to z = 0 galaxies
# For this, exclude BHs that are not BHs at the linking snapshot.
if args.vr_snap is not None:
get_vr_props(args)
bh_vr_snap = np.argmin(np.abs(args.redshifts - args.vr_zred))
print(f"VR snap corresponds to BH output index {bh_vr_snap}.")
bpart_ids_mod = np.copy(bpart_ids)
ind_nobh = np.nonzero(
output_dict['SubgridMasses'][:, bh_vr_snap] * .0 != 0)[0]
bpart_ids_mod[ind_nobh] = -1
vr_file = f'{args.wdir}{args.vr_file}_{args.vr_snap:04d}'
gal_props = xl.connect_to_galaxies(bpart_ids_mod,
vr_file,
extra_props=vr_extra_props)
# Finish galaxy-based analysis
if gal_props is not None:
finish_galaxy_analysis(output_dict, gal_props, args)
else:
gal_props = None
# Write output HDF5 file
write_output_file(output_dict, comment_dict, bpart_ids, bpart_first_output,
gal_props, args)
def main():
print("Parsing input arguments...")
parser = argparse.ArgumentParser(description="Parse input parameters.")
parser.add_argument('sim', help='Simulation index/name to analyse')
parser.add_argument('--bh_bid', type=int,
help='BID of the black hole to highlight.')
parser.add_argument('--base_dir', default=local.BASE_DIR,
help='Base directory of the simulation, default: '
f'{local.BASE_DIR}')
parser.add_argument('--bh_file', default='black_hole_data.hdf5',
help='Name of the file containing the BH data, '
'default: "black_hole_data.hdf5"')
parser.add_argument('--plot_prefix', default='gallery/bh_growth_tracks',
help='Prefix of output files, default: '
'"gallery/bh_growth_tracks')
parser.add_argument('--vrplot_prefix', default='gallery/vr_plots',
help='Prefix of VR plots, default: '
'"gallery/vr_plots".')
parser.add_argument('--plot_mass_range', type=float, nargs='+',
help='Min and max mass of plot range, default: '
'5.0 8.5', default=[5.0, 8.5])
parser.add_argument('--bh_mass_range', type=float, nargs='+',
help='Min and max BH mass for selection, at target z '
'[M_Sun], default: no selection.')
parser.add_argument('--bh_selection_redshift', type=float, default=0.0,
help='Redshift at which to make BH mass selection '
'(default: 0.0)')
parser.add_argument('--include_subdominant_bhs', action='store_true',
help='Only show black holes that are the most massive '
'in their galaxy, at the linked snapshot.')
parser.add_argument('--halo_mstar_range', default=[3e10, 4e11], type=float,
help='Min and max stellar mass of host galaxy '
'[M_Sun], default: 3e10, 4e11.',
nargs='+')
parser.add_argument('--halo_m200_range', default=[0, 1e16], type=float,
help='Min and max M200 mass of host galaxy '
'[M_Sun], default: 0, 1e16.',
nargs='+')
parser.add_argument('--include_satellites', action='store_true',
help='Only show BHs in central haloes, not satellites.')
parser.add_argument('--show_target_only', action='store_true',
help='Show only the target BH, not possible others '
'that also match the selection criteria.')
parser.add_argument('--show_median', action='store_true',
help='Add the median for all selected BHs.')
parser.add_argument('--summary_only', action='store_true',
help='Only generate summary plot, not ones for '
'individual BHs.')
parser.add_argument('--alpha_others', type=float, default=0.25,
help='Alpha value for non-target BHs, default: 0.25')
args = parser.parse_args()
args.wdir = xl.get_sim_dir(args.base_dir, args.sim)
# Name of the input catalogue, containing all the data to plot
args.catloc = f'{args.wdir}{args.bh_file}'
args.plotdata_file = f'{args.wdir}{args.vrplot_prefix}.hdf5'
if os.path.isfile(args.plotdata_file):
bh_list = hd.read_data(args.plotdata_file, 'BlackHoleBIDs')
select_list = None
else:
# Find BHs we are intereste in, load data
select_list = [
["Halo_MStar", '>=', args.halo_mstar_range[0]],
["Halo_MStar", '<', args.halo_mstar_range[1]],
["Halo_M200c", '>=', args.halo_m200_range[0]],
["Halo_M200c", '<', args.halo_m200_range[1]]
]
if not args.include_subdominant_bhs:
select_list.append(['Flag_MostMassiveInHalo', '==', 1])
if not args.include_satellites:
select_list.append(['HaloTypes', '==', 10])
if args.bh_mass_range is not None:
zreds = hd.read_data(args.wdir + args.bh_file, 'Redshifts')
best_index = np.argmin(np.abs(zreds - args.bh_selection_redshift))
print(f"Best index for redshift {args.bh_selection_redshift} is "
f"{best_index}.")
# Subgrid masses are in 10^10 M_sun, so need to adjust selection range
select_list.append(
['SubgridMasses', '>=', args.bh_mass_range[0]/1e10, best_index])
select_list.append(
['SubgridMasses', '<=', args.bh_mass_range[1]/1e10, best_index])
bh_list = None
bh_data, bh_sel = xl.lookup_bh_data(args.wdir + args.bh_file,
bh_props_list, select_list)
if bh_list is None:
bh_list = bh_sel
generate_track_image(args, bh_data, bh_list)
if args.bh_bid is None:
# No target specified: process all selected BHs
for iibh, ibh in enumerate(bh_list):
generate_track_image(args, bh_data, bh_list, iibh)
else:
# Only plot the selected BH.
iibh = np.nonzero(bh_list == args.bh_bid)[0]
if len(iibh) == 1:
generate_track_image(args, bh_data, bh_list, iibh[0])
else:
print(f"Could not (unambiguously) find BH-BID {args.bh_bid} "
f"in BH list.")
print("Done!")
def process_sim(isim, args):
"""Process one individual simulation."""
if args.have_full_sim_dir:
args.wdir = isim
else:
args.wdir = xl.get_sim_dir(args.base_dir, isim)
print(f"Analysing simulation {args.wdir}...")
# Name of the input catalogue, containing all the data to plot
args.catloc = f'{args.wdir}{args.bh_file}'
bh_props_list = ['ParticleIDs']
# Select BHs in this sim
args.plotdata_file = f'{args.wdir}{args.vrplot_prefix}.hdf5'
if os.path.isfile(args.plotdata_file):
bh_list = hd.read_data(args.plotdata_file, 'BlackHoleBIDs')
select_list = None
elif args.bh_bid is None:
# Find BHs we are intereste in, load data
select_list = [
["Halo_MStar", '>=', args.halo_mstar_range[0]],
["Halo_MStar", '<', args.halo_mstar_range[1]],
["Halo_M200c", '>=', args.halo_m200_range[0]],
["Halo_M200c", '<', args.halo_m200_range[1]]
]
if not args.include_subdominant_bhs:
select_list.append(['Flag_MostMassiveInHalo', '==', 1])
if not args.include_satellites:
select_list.append(['HaloTypes', '==', 10])
if args.bh_mass_range is not None:
zreds = hd.read_data(args.wdir + args.bh_file, 'Redshifts')
best_index = np.argmin(np.abs(zreds - args.bh_selection_redshift))
print(f"Best index for redshift {args.bh_selection_redshift} is "
f"{best_index}.")
# Subgrid masses are in 10^10 M_sun, so need to adjust selection
# range
select_list.append(
['SubgridMasses', '>=', args.bh_mass_range[0]/1e10, best_index])
select_list.append(
['SubgridMasses', '<=', args.bh_mass_range[1]/1e10, best_index])
bh_list = None
else:
bh_list = args.bh_bid
select_list = None
bh_file = args.wdir + args.bh_file
bh_data, bh_sel = xl.lookup_bh_data(bh_file, bh_props_list, select_list)
if bh_list is None:
bh_list = bh_sel
if len(bh_list) == 0:
print("No black holes selected, aborting.")
return
# Overwrite selection with input, if specific BID(s) provided
if args.bh_bid is not None:
bh_list = args.bh_bid
for isnap in args.snapshots:
process_snapshot(args, bh_list, bh_data, isim, isnap)
def process_sim(isim, args):
"""Process one specific simulation."""
if args.have_full_sim_dir:
args.wdir = isim
else:
args.wdir = xl.get_sim_dir(args.base_dir, isim)
print(f"Analysing simulation {args.wdir}...")
# Name of the input catalogue, containing all the data to plot
args.catloc = f'{args.wdir}{args.bh_file}'
# Select BHs in this sim
args.plotdata_file = f'{args.wdir}{args.vrplot_prefix}.hdf5'
if os.path.isfile(args.plotdata_file):
bh_list = hd.read_data(args.plotdata_file, 'BlackHoleBIDs')
select_list = None
elif args.bh_bid is not None:
select_list = None
bh_list = args.bh_bid
else:
# Find BHs we are intereste in, load data
select_list = [["Halo_MStar", '>=', args.halo_mstar_range[0]],
["Halo_MStar", '<', args.halo_mstar_range[1]],
["Halo_M200c", '>=', args.halo_m200_range[0]],
["Halo_M200c", '<', args.halo_m200_range[1]]]
if not args.include_subdominant_bhs:
select_list.append(['Flag_MostMassiveInHalo', '==', 1])
if not args.include_satellites:
select_list.append(['HaloTypes', '==', 10])
if args.bh_mass_range is not None:
zreds = hd.read_data(args.wdir + args.bh_file, 'Redshifts')
best_index = np.argmin(np.abs(zreds - args.bh_selection_redshift))
print(f"Best index for redshift {args.bh_selection_redshift} is "
f"{best_index}.")
# Subgrid masses are in 10^10 M_sun, so need to adjust selection
# range
select_list.append([
'SubgridMasses', '>=', args.bh_mass_range[0] / 1e10, best_index
])
select_list.append([
'SubgridMasses', '<=', args.bh_mass_range[1] / 1e10, best_index
])
bh_list = None
bh_file = args.wdir + args.bh_file
bh_data, bh_list = xl.lookup_bh_data(bh_file, bh_props_list, select_list,
bh_list)
args.nsnap = len(bh_data['Times'])
# Extract meta-data from Header
bh_data['CodeBranch'] = hd.read_attribute(bh_file, 'Code', 'Git Branch')
bh_data['CodeDate'] = hd.read_attribute(bh_file, 'Code', 'Git Date')
bh_data['CodeRev'] = hd.read_attribute(bh_file, 'Code', 'Git Revision')
bh_data['SimName'] = hd.read_attribute(bh_file, 'Header', 'RunName')
# Look up stars data
stars = Stars(args)
for ibh in bh_list:
process_bh(args, stars, bh_data, ibh, isim)