def timeline(input_path, halo_ID):
redshift, place = ot.z(input_path)
redshift = redshift[::-1]
z_1 = []
position = []
ID = []
mstar = []
mhalo = []
z_2 = []
descendant = []
filename = input_path + '/halo_' + str(halo_ID) + '.txt'
file_halo = open(filename, 'r')
for line in file_halo.readlines():
data = map(float, np.array(line.split()))
z_1.append(data[0])
position.append(redshift.tolist().index(data[0]))
ID.append(int(data[1]))
mstar.append(data[2])
mhalo.append(data[3])
z_2.append(data[4])
descendant.append(int(data[5]))
file_halo.close()
timeline_file = input_path + '/timeline_' + str(halo_ID) + '.txt'
write_data = open(timeline_file, 'w')
ID_filter = halo_ID
for i in range(len(redshift)):
data = [[a, b, c, d, e]
for a, b, c, d, e in zip(z_1, ID, mstar, mhalo, descendant)
if a == redshift[i] if b == ID_filter]
if data:
if data[0] != -1:
write_data.write(
'%-6.3f %-5i %-10.3e %-10.3e \n' %
(data[0][0], data[0][1], data[0][2], data[0][3]))
#print '%-6.3f %-5i %-10.3e %-10.3e' %(data[0][0],data[0][1],data[0][2],data[0][3])
ID_filter = data[0][4]
else:
break
write_data.close()
def tree(halo_path, output_path, snapshot_path, input_path, dummy, halo_ID,
start, finish, ncpu, rvir_per):
list_order = [halo_ID]
redshift_list = []
iHalo_list = []
fname = input_path + '/halo_' + str(halo_ID) + '.txt'
redshift_list, iHalo_list = np.genfromtxt(fname,
delimiter='',
usecols=(0, 1),
skip_header=0,
unpack=True)
iHalo_list = iHalo_list.astype(int)
redshift = np.round(list(OrderedDict.fromkeys(redshift_list)), 3)
z, position = ot.z(input_path)
i = []
iHalo_new = []
for j in range(len(redshift)):
pos = z.tolist().index(redshift[j])
i.append(pos)
sel = (redshift_list == redshift[j])
iHalo_new.append(iHalo_list[sel])
new_array = zip(i, iHalo_new)
comoving = 1
halodir = halo_path + '/'
snap_dir = snapshot_path + '/snapdir_'
savepath = output_path + '/'
new_input = new_array[start:finish]
Parallel(n_jobs=ncpu)(delayed(filter_Rvir)(
halodir, snap_dir, savepath, i, comoving, iHalo_list, rvir_per, dummy)
for i, iHalo_list in new_input)
def run(read_path, halo_path, save_path, sim_suite, sim_name, merger, halo_ID,
filter_value, filter_flag):
# starts from z=12 to z=0
z_path = read_path
regime_list = ['cold', 'total']
radius_list = [0.1, 0.2, 0.5, 1.0, 10.0]
radius_name_list = ['0.1', '0.2', '0.5', '1.0', 'rvir']
mass_retention_list = [0.05, 0.1, 0.25, 0.5, 1.0]
seed_mass_list = [1e1, 1e2, 1e3, 1e4, 1e5]
seed_mass_name_list = ['1e1', '1e2', '1e3', '1e4', '1e5']
if merger == 1:
if filter_flag == 0:
filename = read_path + '/' + sim_name + '_merger.hdf5'
if filter_flag == 1:
filename = read_path + '/' + sim_name + '_all.hdf5'
if merger == 0:
filename = read_path + '/' + sim_name + '_nomerger.hdf5'
f = h5py.File(filename, 'a')
# Timeline file data
# starts from z=0 to z=12
filename_timeline = read_path + '/timeline_' + str(halo_ID) + '.txt'
z_1_timeline, ID_timeline, mhalo_timeline = np.genfromtxt(
filename_timeline,
delimiter='',
usecols=(0, 1, 3),
skip_header=0,
unpack=True)
ID_timeline = ID_timeline.astype(int)
# reverse data now it is from 12 to 0, timeline data is REVERSE
z_1_timeline = z_1_timeline[::-1]
ID_timeline = ID_timeline[::-1]
mhalo_timeline = mhalo_timeline[::-1]
# Bulge data read
# starts from z=12 to z=0
bdd_data = h5py.File(save_path + '/BDD_data.hdf5', 'r')
data = {}
for regime in regime_list:
for radius_name in radius_name_list:
prefix = 'halo_' + str(
halo_ID
) + '/' + regime + '/' + sim_suite + '/' + sim_name + '/' + radius_name
if radius_name == 'rvir':
data[regime + '/' + radius_name +
'/radius'] = bdd_data[prefix + '/radius'].value
data[regime + '/' + radius_name +
'/rvir_hgm'] = bdd_data[prefix + '/rvir_hgm'].value
data[regime + '/' + radius_name +
'/rvir_hsm'] = bdd_data[prefix + '/rvir_hsm'].value
data[regime + '/' + radius_name +
'/rvir_hsf'] = bdd_data[prefix + '/rvir_hsf'].value
data[regime + '/' + radius_name + '/z_1'] = bdd_data[prefix +
'/z_1'].value
data[regime + '/' + radius_name + '/ID'] = bdd_data[prefix +
'/ID'].value
data[regime + '/' + radius_name +
'/mgas'] = bdd_data[prefix + '/mgas'].value
data[regime + '/' + radius_name +
'/mstar'] = bdd_data[prefix + '/mstar'].value
data[regime + '/' + radius_name +
'/mhalo'] = bdd_data[prefix + '/mhalo'].value
data[regime + '/' + radius_name +
'/mbulge'] = bdd_data[prefix + '/mbulge'].value
data[regime + '/' + radius_name +
'/mdisk'] = bdd_data[prefix + '/mdisk'].value
data[regime + '/' + radius_name +
'/fbulge'] = bdd_data[prefix + '/fbulge'].value
data[regime + '/' + radius_name +
'/fdisk'] = bdd_data[prefix + '/fdisk'].value
data[regime + '/' + radius_name +
'/inflow'] = bdd_data[prefix + '/inflow'].value
data[regime + '/' + radius_name +
'/outflow'] = bdd_data[prefix + '/outflow'].value
data[regime + '/' + radius_name +
'/bh_vel'] = bdd_data[prefix + '/bh_vel'].value
data[regime + '/' + radius_name +
'/vel_disp'] = bdd_data[prefix + '/vel_disp'].value
data[regime + '/' + radius_name +
'/num_new_stars'] = bdd_data[prefix + '/num_new_stars'].value
data[regime + '/' + radius_name + '/sfr'] = bdd_data[prefix +
'/sfr'].value
data[regime + '/' + radius_name +
'/ssfr'] = bdd_data[prefix + '/ssfr'].value
data[regime + '/' + radius_name +
'/mean_temp'] = bdd_data[prefix + '/mean_temp'].value
data[regime + '/' + radius_name +
'/mean_rho'] = bdd_data[prefix + '/mean_rho'].value
data[regime + '/' + radius_name +
'/mean_gas_surf_density'] = bdd_data[
prefix + '/mean_gas_surf_density'].value
data[regime + '/' + radius_name +
'/mean_star_surf_density'] = bdd_data[
prefix + '/mean_star_surf_density'].value
data[regime + '/' + radius_name +
'/mean_cs'] = bdd_data[prefix + '/mean_cs'].value
#data[regime + '/' + radius_name + '/mean_nh'] = bdd_data[prefix + '/mean_nh'].value
data[regime + '/' + radius_name +
'/mean_vel'] = bdd_data[prefix + '/mean_vel'].value
data[regime + '/' + radius_name +
'/mean_vel_phi'] = bdd_data[prefix + '/mean_vel_phi'].value
redshift, place = ot.z(z_path)
# WITH MERGERS
if merger == 1:
# Halo file data
# starts from z=0 to z=12
filename_halo = read_path + '/halo_' + str(halo_ID) + '.txt'
z_1 = []
z_2 = []
position = []
ID = []
descendant = []
mhalo = []
# but we read it in reverse order, now data starts from z=12 to z=0
file_halo = open(filename_halo, 'r')
for line in reversed(file_halo.readlines()):
data_halo = map(float, np.array(line.split()))
z_1.append(data_halo[0])
position.append(redshift.tolist().index(data_halo[0]))
ID.append(int(data_halo[1]))
mhalo.append(data_halo[3])
z_2.append(data_halo[4])
descendant.append([int(ii) for ii in data_halo[5:]])
file_halo.close()
# WITHOUT MERGERS
if merger == 0:
z_1, ID, mhalo = np.genfromtxt(filename_timeline,
delimiter='',
usecols=(0, 1, 3),
skip_header=0,
unpack=True)
ID = ID.astype(int)
z_1 = z_1[::-1]
ID = ID[::-1]
mhalo = mhalo[::-1]
position = [place[redshift.tolist().index(xxx)] for xxx in z_1]
z_2 = np.insert(z_1[:-1], 0,
redshift[redshift.tolist().index(z_1[0]) - 1])
descendant = np.insert(ID[:-1], 0, -1)
descendant = [[xxx] for xxx in descendant]
z_1 = z_1.tolist()
ID = ID.tolist()
mhalo = mhalo.tolist()
z_2 = z_2.tolist()
for regime in regime_list:
for radius, radius_name in zip(radius_list, radius_name_list):
xx0 = data[regime + '/' + radius_name + '/z_1']
xx1 = data[regime + '/' + radius_name + '/ID']
xx2 = data[regime + '/' + radius_name + '/mgas']
xx3 = data[regime + '/' + radius_name + '/mstar']
xx4 = data[regime + '/' + radius_name + '/mhalo']
xx5 = data[regime + '/' + radius_name + '/mbulge']
xx6 = data[regime + '/' + radius_name + '/mdisk']
xx7 = data[regime + '/' + radius_name + '/fbulge']
xx8 = data[regime + '/' + radius_name + '/fdisk']
xx9 = data[regime + '/' + radius_name + '/inflow']
xx10 = data[regime + '/' + radius_name + '/outflow']
xx11 = data[regime + '/' + radius_name + '/bh_vel']
xx12 = data[regime + '/' + radius_name + '/vel_disp']
xx13 = data[regime + '/' + radius_name + '/num_new_stars']
xx14 = data[regime + '/' + radius_name + '/sfr']
xx15 = data[regime + '/' + radius_name + '/ssfr']
xx16 = data[regime + '/' + radius_name + '/mean_temp']
xx17 = data[regime + '/' + radius_name + '/mean_rho']
xx18 = data[regime + '/' + radius_name + '/mean_gas_surf_density']
xx19 = data[regime + '/' + radius_name + '/mean_star_surf_density']
xx20 = data[regime + '/' + radius_name + '/mean_cs']
#xx20 = data[regime + '/' + radius_name + '/mean_nh']
xx21 = data[regime + '/' + radius_name + '/mean_vel']
xx22 = data[regime + '/' + radius_name + '/mean_vel_phi']
# Filter timeline from halo data
if radius_name == 'rvir':
xx23 = data[regime + '/' + radius_name + '/radius']
xx24 = data[regime + '/' + radius_name + '/rvir_hgm']
xx25 = data[regime + '/' + radius_name + '/rvir_hsm']
xx26 = data[regime + '/' + radius_name + '/rvir_hsf']
Z_ID = zip(xx0, xx1)
Z_ID_timeline = zip(z_1_timeline, ID_timeline)
if radius_name != 'rvir':
zeytin_store = zip(Z_ID, xx0, xx1, xx2, xx3, xx4, xx5, xx6,
xx7, xx8, xx9, xx10, xx11, xx12, xx13, xx14,
xx15, xx16, xx17, xx18, xx19, xx20, xx21,
xx22)
zeytin = [[
a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13,
a14, a15, a16, a17, a18, a19, a20, a21, a22, a23
] for a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12,
a13, a14, a15, a16, a17, a18, a19, a20, a21, a22, a23
in zeytin_store if a0 in Z_ID_timeline]
else:
zeytin_store = zip(Z_ID, xx0, xx1, xx2, xx3, xx4, xx5, xx6,
xx7, xx8, xx9, xx10, xx11, xx12, xx13, xx14,
xx15, xx16, xx17, xx18, xx19, xx20, xx21,
xx22, xx23, xx24, xx25, xx26)
zeytin = [[
a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13,
a14, a15, a16, a17, a18, a19, a20, a21, a22, a23, a24, a25,
a26, a27
] for a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12,
a13, a14, a15, a16, a17, a18, a19, a20, a21, a22,
a23, a24, a25, a26, a27 in zeytin_store
if a0 in Z_ID_timeline]
x_z1 = np.array(zip(*zeytin)[0])
x_ID = np.array(zip(*zeytin)[1])
x_mgas = np.array(zip(*zeytin)[2])
x_mstar = np.array(zip(*zeytin)[3])
x_mhalo = np.array(zip(*zeytin)[4])
x_mbulge = np.array(zip(*zeytin)[5])
x_mdisk = np.array(zip(*zeytin)[6])
x_fbulge = np.array(zip(*zeytin)[7])
x_fdisk = np.array(zip(*zeytin)[8])
x_inflow = np.array(zip(*zeytin)[9])
x_outflow = np.array(zip(*zeytin)[10])
x_bh_vel = np.array(zip(*zeytin)[11])
x_vel_disp = np.array(zip(*zeytin)[12])
x_numns = np.array(zip(*zeytin)[13])
x_sfr = np.array(zip(*zeytin)[14])
x_ssfr = np.array(zip(*zeytin)[15])
x_temp = np.array(zip(*zeytin)[16])
x_rho = np.array(zip(*zeytin)[17])
x_gas_sd = np.array(zip(*zeytin)[18])
x_star_sd = np.array(zip(*zeytin)[19])
x_cs = np.array(zip(*zeytin)[20])
#x_nh = np.array(zip(*zeytin)[20])
x_vel = np.array(zip(*zeytin)[21])
x_vel_phi = np.array(zip(*zeytin)[22])
if radius_name == 'rvir':
x_r = np.array(zip(*zeytin)[23])
x_rhgm = np.array(zip(*zeytin)[24])
x_rhsm = np.array(zip(*zeytin)[25])
x_rhsf = np.array(zip(*zeytin)[26])
ext = 'halo_' + str(halo_ID) + '/' + regime + '/' + radius_name
f[ext + '/z_1'] = x_z1
f[ext + '/ID'] = x_ID
f[ext + '/mgas'] = x_mgas
f[ext + '/mstar'] = x_mstar
f[ext + '/mhalo'] = x_mhalo
f[ext + '/mbulge'] = x_mbulge
f[ext + '/mdisk'] = x_mdisk
f[ext + '/fbulge'] = x_fbulge
f[ext + '/fdisk'] = x_fdisk
f[ext + '/inflow'] = x_inflow
f[ext + '/outflow'] = x_outflow
f[ext + '/bh_vel'] = x_bh_vel
f[ext + '/vel_disp'] = x_vel_disp
f[ext + '/num_new_stars'] = x_numns
f[ext + '/sfr'] = x_sfr
f[ext + '/ssfr'] = x_ssfr
f[ext + '/mean_temp'] = x_temp
f[ext + '/mean_rho'] = x_rho
f[ext + '/mean_gas_surf_density'] = x_gas_sd
f[ext + '/mean_star_surf_density'] = x_star_sd
f[ext + '/mean_cs'] = x_cs
#f[ext + '/mean_nh'] = x_nh
f[ext + '/mean_vel'] = x_vel
f[ext + '/mean_vel_phi'] = x_vel_phi
if radius_name == 'rvir':
f[ext + '/mhalo_rvir'] = mhalo_timeline
f[ext + '/radius'] = x_r
f[ext + '/rvir_hgm'] = x_rhgm
f[ext + '/rvir_hsm'] = x_rhsm
f[ext + '/rvir_hsf'] = x_rhsf
if merger == 1:
fdisk = data[regime + '/' + radius_name + '/fdisk']
mgas = data[regime + '/' + radius_name + '/mgas']
mstar = data[regime + '/' + radius_name + '/mstar']
mhalo = data[regime + '/' + radius_name + '/mhalo']
mean_cs = data[regime + '/' + radius_name + '/mean_cs']
#mean_nh = data[regime + '/' + radius_name + '/mean_nh']
mean_vel = data[regime + '/' + radius_name + '/mean_vel']
mean_vel_phi = data[regime + '/' + radius_name +
'/mean_vel_phi']
mean_rho = data[regime + '/' + radius_name + '/mean_rho']
sfr = data[regime + '/' + radius_name + '/sfr']
if merger == 0:
fdisk = x_fdisk
mgas = x_mgas
mstar = x_mstar
mhalo = x_mhalo
mean_cs = x_cs
#mean_nh = x_nh
mean_vel = x_vel
mean_vel_phi = x_vel_phi
mean_rho = x_rho
sfr = x_sfr
for mass_retention in mass_retention_list:
for seed_mass, seed_mass_name in zip(seed_mass_list,
seed_mass_name_list):
mdot_torque, mdot_bondi, mdot_tremmel, mdot_hobbs, mdot_dyn, mdot_sfr, f_torque_edd, f_bondi_edd, f_tremmel_edd, f_hobbs_edd, f_dyn_edd, f_sfr_edd, m_bh_torque, m_bh_bondi, m_bh_tremmel, m_bh_hobbs, m_bh_dyn, m_bh_sfr, mass_seed = ot.MERGER(
z_1,
ID,
z_2,
descendant,
halo_path,
redshift,
place,
z_1_timeline,
ID_timeline,
fdisk,
mhalo,
mstar,
mgas,
radius,
mean_cs,
mean_vel,
mean_vel_phi,
mean_rho,
sfr,
mass_retention,
seed_mass,
merger,
supp=0,
filter_for_timeline=filter_value)
mdot_torque_supp, mdot_bondi_supp, mdot_tremmel_supp, mdot_hobbs_supp, mdot_dyn_supp, mdot_sfr_supp, f_torque_edd_supp, f_bondi_edd_supp, f_tremmel_edd_supp, f_hobbs_edd_supp, f_dyn_edd_supp, f_sfr_edd_supp, m_bh_torque_supp, m_bh_bondi_supp, m_bh_tremmel_supp, m_bh_hobbs_supp, m_bh_dyn_supp, m_bh_sfr_supp, mass_seed_supp = ot.MERGER(
z_1,
ID,
z_2,
descendant,
halo_path,
redshift,
place,
z_1_timeline,
ID_timeline,
fdisk,
mhalo,
mstar,
mgas,
radius,
mean_cs,
mean_vel,
mean_vel_phi,
mean_rho,
sfr,
mass_retention,
seed_mass,
merger,
supp=1,
filter_for_timeline=filter_value)
prefix = 'halo_' + str(
halo_ID
) + '/' + regime + '/r_' + radius_name + '/mass_ret_' + str(
mass_retention) + '/seed_' + seed_mass_name
f[prefix + '/mdot_torque'] = mdot_torque
f[prefix + '/mdot_bondi'] = mdot_bondi
f[prefix + '/mdot_tremmel'] = mdot_tremmel
f[prefix + '/mdot_hobbs'] = mdot_hobbs
f[prefix + '/mdot_dyn'] = mdot_dyn
f[prefix + '/mdot_sfr'] = mdot_sfr
f[prefix + '/f_torque_edd'] = f_torque_edd
f[prefix + '/f_bondi_edd'] = f_bondi_edd
f[prefix + '/f_tremmel_edd'] = f_tremmel_edd
f[prefix + '/f_hobbs_edd'] = f_hobbs_edd
f[prefix + '/f_dyn_edd'] = f_dyn_edd
f[prefix + '/f_sfr_edd'] = f_sfr_edd
f[prefix + '/m_bh_torque'] = m_bh_torque
f[prefix + '/m_bh_bondi'] = m_bh_bondi
f[prefix + '/m_bh_tremmel'] = m_bh_tremmel
f[prefix + '/m_bh_hobbs'] = m_bh_hobbs
f[prefix + '/m_bh_dyn'] = m_bh_dyn
f[prefix + '/m_bh_sfr'] = m_bh_sfr
f[prefix + '/mass_seed'] = mass_seed
f[prefix + '/mdot_torque_supp'] = mdot_torque_supp
f[prefix + '/mdot_bondi_supp'] = mdot_bondi_supp
f[prefix + '/mdot_tremmel_supp'] = mdot_tremmel_supp
f[prefix + '/mdot_hobbs_supp'] = mdot_hobbs_supp
f[prefix + '/mdot_dyn_supp'] = mdot_dyn_supp
f[prefix + '/mdot_sfr_supp'] = mdot_sfr_supp
f[prefix + '/f_torque_edd_supp'] = f_torque_edd_supp
f[prefix + '/f_bondi_edd_supp'] = f_bondi_edd_supp
f[prefix + '/f_tremmel_edd_supp'] = f_tremmel_edd_supp
f[prefix + '/f_hobbs_edd_supp'] = f_hobbs_edd_supp
f[prefix + '/f_dyn_edd_supp'] = f_dyn_edd_supp
f[prefix + '/f_sfr_edd_supp'] = f_sfr_edd
f[prefix + '/m_bh_torque_supp'] = m_bh_torque_supp
f[prefix + '/m_bh_bondi_supp'] = m_bh_bondi_supp
f[prefix + '/m_bh_tremmel_supp'] = m_bh_tremmel_supp
f[prefix + '/m_bh_hobbs_supp'] = m_bh_hobbs_supp
f[prefix + '/m_bh_dyn_supp'] = m_bh_dyn_supp
f[prefix + '/m_bh_sfr_supp'] = m_bh_sfr_supp
f[prefix + '/mass_seed_supp'] = mass_seed_supp
f.close()
halo_path = str(sys.argv[1])
snapshot_path = str(sys.argv[2])
read_path = str(sys.argv[3])
output_path = str(sys.argv[4])
dummy = int(sys.argv[5])
ncpu = int(sys.argv[6])
remove = len(read_path) + 6
filelist = sorted(glob.glob(read_path + '/halo_*.txt'))
halo_ID_list = []
for f in filelist:
halo_ID_list.append(f[remove:-4])
halo_ID_list = np.sort(np.array(halo_ID_list[:-1]).astype(int))[1:]
for halo_ID in halo_ID_list:
redshift, place = ot.z(read_path)
filename = read_path + '/halo_' + str(halo_ID) + '.txt'
z_1, ID = np.genfromtxt(filename,
delimiter='',
usecols=(0, 1),
unpack=True)
ID = ID.astype(int)
position = np.array([redshift.tolist().index(a) + dummy for a in z_1])
k_list = range(0, len(ID))
Parallel(n_jobs=ncpu)(
delayed(onur)(snapshot_path, halo_path, output_path, z_1, ID, halo_ID,
position, k, redshift, dummy) for k in k_list)
def tree_list(snapshot_path, input_path, halo_path, halo_ID, dummy):
redshift, position = ot.z(input_path)
redshift = redshift[::-1]
position = position.astype(int)[::-1]
halo_file = input_path + '/halo_' + str(halo_ID) + '.txt'
write_data = open(halo_file, 'w')
for j in range(len(redshift)):
if j == 0:
halofile = glob.glob(halo_path + '/' + str('%03d' % position[j]) +
'/*AHF_halos')[0]
snapshot_dir = snapshot_path + '/snapdir_' + str(
'%03d' % position[j])
if dummy == 178:
star = ot.readsnap_compact(snapshot_dir,
snum=position[j],
ptype=4,
cosmological=1)
header = ot.readsnap_compact(snapshot_dir,
snum=position[j],
ptype=0,
header_only=1)
else:
star = ot.readsnap(snapshot_dir,
snum=position[j],
ptype=4,
cosmological=1)
header = ot.readsnap(snapshot_dir,
snum=position[j],
ptype=0,
header_only=1)
hinv = 1.0 / header["hubble"]
a_scale = 1.0 / (1.0 + header["redshift"])
constant = a_scale * hinv
ID, mhalo, rvir, fMhires, mstar = np.genfromtxt(halofile,
delimiter='',
usecols=(0, 3, 11,
37, 64),
skip_header=1,
unpack=True)
data = [[a, b, c, d, e]
for a, b, c, d, e in zip(ID, mhalo, rvir, fMhires, mstar)
if a == halo_ID]
center = np.array(ot.get_haloCenter(halofile, halo_ID)) * constant
star["p"] -= center
ID_new = int(data[0][0])
mhalo_new = data[0][1] * hinv
rvir_new = data[0][2] * constant
fMhires_new = data[0][3]
mstar_new = data[0][4] * hinv
flag, m_halo, m_star = constraints(star["p"], star["m"], rvir_new,
mhalo_new, fMhires_new,
mstar_new)
new_ID_list = [halo_ID]
new_mhalo_list = [m_halo]
new_mstar_list = [m_star]
else:
filename = glob.glob(halo_path + '/' + str('%03d' % position[j]) +
'/*AHF_mtree_idx')[0]
halofile = glob.glob(halo_path + '/' + str('%03d' % position[j]) +
'/*AHF_halos')[0]
snapshot_dir = snapshot_path + '/snapdir_' + str(
'%03d' % position[j])
# STAR AND HEADER DATA READ
if dummy == 178:
star = ot.readsnap_compact(snapshot_dir,
snum=position[j],
ptype=4,
cosmological=1)
header = ot.readsnap_compact(snapshot_dir,
snum=position[j],
ptype=0,
header_only=1)
else:
star = ot.readsnap(snapshot_dir,
snum=position[j],
ptype=4,
cosmological=1)
header = ot.readsnap(snapshot_dir,
snum=position[j],
ptype=0,
header_only=1)
hinv = 1.0 / header["hubble"]
a_scale = 1.0 / (1.0 + header["redshift"])
constant = a_scale * hinv
# READ IDX DATA AND FILTER
ID_0, ID_1 = np.genfromtxt(filename,
delimiter='',
usecols=(0, 1),
skip_header=1,
unpack=True,
dtype=int)
# READ HALO INFO
ID_halo, mhalo_halo, rvir_halo, fMhires_halo, mstar_halo = np.genfromtxt(
halofile,
delimiter='',
usecols=(0, 3, 11, 37, 64),
skip_header=1,
unpack=True)
zip_halo = zip(ID_halo, mhalo_halo, rvir_halo, fMhires_halo,
mstar_halo)
iHalo_list = []
mhalo_list = []
mstar_list = []
flag_lastline = 0
for iHalo, mhalo, mstar in zip(new_ID_list, new_mhalo_list,
new_mstar_list):
temp_iHalo_list = [a for a, b in zip(ID_0, ID_1) if b == iHalo]
temp_flag_list = []
temp_mhalo_list = []
temp_mstar_list = []
for halo_ID in temp_iHalo_list:
center = np.array(ot.get_haloCenter(halofile,
halo_ID)) * constant
pos = star["p"] - center
data = [[a, b, c, d, e] for a, b, c, d, e in zip_halo
if a == halo_ID]
ID_new = int(data[0][0])
mhalo_new = data[0][1] * hinv
rvir_new = data[0][2] * constant
fMhires_new = data[0][3]
mstar_new = data[0][4] * hinv
flag, m_halo, m_star = constraints(pos, star["m"],
rvir_new, mhalo_new,
fMhires_new, mstar_new)
temp_flag_list.append(flag)
temp_mhalo_list.append(m_halo)
temp_mstar_list.append(m_star)
# filter candidates with their flag values
temp_iHalo_list = [
a for a, b in zip(temp_iHalo_list, temp_flag_list)
if b == 1
]
temp_mhalo_list = [
a for a, b in zip(temp_mhalo_list, temp_flag_list)
if b == 1
]
temp_mstar_list = [
a for a, b in zip(temp_mstar_list, temp_flag_list)
if b == 1
]
if temp_iHalo_list:
write_data.write(
'%-6.3f %-5i %-10.3e %-10.3e %-6.3f %-30s \n' %
(redshift[j - 1], iHalo, mstar, mhalo, redshift[j],
' '.join(map(str, temp_iHalo_list))))
else:
write_data.write(
'%-6.3f %-5i %-10.3e %-10.3e %-6.3f %-30s \n' %
(redshift[j - 1], iHalo, mstar, mhalo, redshift[j],
'-1'))
iHalo_list += temp_iHalo_list
mhalo_list += temp_mhalo_list
mstar_list += temp_mstar_list
if j == len(redshift) - 1:
if temp_iHalo_list:
flag_lastline = 1
# for iHalo, mhalo, mstar in zip(temp_iHalo_list, temp_mhalo_list, temp_mstar_list):
# print '%-6.3f %-5i %-10.3e %-10.3e %-6.3f %-30s \n' %(redshift[j], iHalo, mstar, mhalo, redshift[j]+0.4, '-1')
new_ID_list = iHalo_list
new_mhalo_list = mhalo_list
new_mstar_list = mstar_list
if flag_lastline == 1:
for iHalo, mhalo, mstar in zip(new_ID_list, new_mhalo_list,
new_mstar_list):
write_data.write(
'%-6.3f %-5i %-10.3e %-10.3e %-6.3f %-30s \n' %
(redshift[-1], iHalo, mstar, mhalo, redshift[-1] + 0.4,
'-1'))
write_data.close()