def main(model_dir, outdir, redshift_table, subvols, obsdir):
# Loop over redshift and subvolumes
plt = common.load_matplotlib()
fields = {'galaxies': ('mstars_disk', 'mstars_bulge', 'mvir_hosthalo',
'mvir_subhalo', 'type', 'mean_stellar_age',
'sfr_disk', 'sfr_burst', 'id_galaxy')}
z = (0, 0.25, 0.5, 1, 1.5, 2.0, 3.0, 4.0, 6.0)
snapshots = redshift_table[z]
# Create histogram
for index, snapshot in enumerate(snapshots):
hdf5_data = common.read_data(model_dir, snapshot, fields, subvols)
#sfh, delta_t, LBT = common.read_sfh(model_dir, snapshot, sfh_fields, subvols)
seds, ids, nbands = common.read_photometry_data(model_dir, snapshot, subvols)
if(index == 0):
CSED = np.zeros(shape = (len(z), 5, nbands))
prepare_data(hdf5_data, seds, ids, CSED, nbands, index)
h0, volh = hdf5_data[0], hdf5_data[1]
if(volh > 0.):
CSED[index,:] = CSED[index,:] / volh * pow(h0,3.0)
# Take logs
plot_csed(plt, outdir, obsdir, h0, CSED, nbands)
def main(model_dir, output_dir, redshift_table, subvols, obs_dir):
plt = common.load_matplotlib()
fields = {
'galaxies':
('type', 'mstars_disk', 'mstars_bulge', 'rstar_disk', 'm_bh',
'matom_disk', 'mmol_disk', 'mgas_disk', 'matom_bulge', 'mmol_bulge',
'mgas_bulge', 'mvir_hosthalo')
}
hdf5_data = common.read_data(model_dir, redshift_table[0], fields, subvols)
(mgas_relation, mgas_relation_cen, mgas_relation_sat, mh2_gals, mh1_gals,
mgas_gals, mh2_relation, mh1_relation, mhr_relation, mhr_relation_cen,
mhr_relation_sat, mgas_relation_ltg, mh2_relation_ltg, mh1_relation_ltg,
mgas_relation_etg, mh2_relation_etg, mh1_relation_etg,
mgas_ms_relation_ltg, mh2_ms_relation_ltg, mh1_ms_relation_ltg,
mgas_ms_relation_etg, mh2_ms_relation_etg, mh1_ms_relation_etg,
mh1_relation_satellites_halos) = prepare_data(hdf5_data)
plot_cold_gas_fraction(plt, output_dir, obs_dir, mgas_relation,
mgas_relation_cen, mgas_relation_sat)
plot_HI_stacking(plt, output_dir, obs_dir, mh1_relation_satellites_halos)
plot_molecular_gas_fraction(
plt, output_dir, obs_dir, mgas_gals, mgas_relation, mh1_gals,
mh1_relation, mh2_gals, mh2_relation, mgas_relation_ltg,
mh2_relation_ltg, mh1_relation_ltg, mgas_relation_etg,
mh2_relation_etg, mh1_relation_etg, mgas_ms_relation_ltg,
mh2_ms_relation_ltg, mh1_ms_relation_ltg, mgas_ms_relation_etg,
mh2_ms_relation_etg, mh1_ms_relation_etg)
plot_h1h2_gas_fraction(plt, output_dir, mhr_relation, mhr_relation_cen,
mhr_relation_sat)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
plt = common.load_matplotlib()
fields = {
'global':
('redshifts', 'm_hi', 'm_h2', 'mcold', 'mcold_metals', 'mhot_halo',
'mejected_halo', 'mstars', 'mstars_bursts_mergers',
'mstars_bursts_diskinstabilities', 'm_bh', 'sfr_quiescent',
'sfr_burst', 'm_dm', 'mcold_halo', 'number_major_mergers',
'number_minor_mergers', 'number_disk_instabilities', 'smbh_maximum')
}
# Read data from each subvolume at a time and add it up
# rather than appending it all together
for idx, subvol in enumerate(subvols):
subvol_data = common.read_data(modeldir, redshift_table[0], fields,
[subvol])
max_bhs_subvol = subvol_data[20].copy()
if idx == 0:
hdf5_data = subvol_data
max_smbh = max_bhs_subvol
else:
max_smbh = np.maximum(max_smbh, max_bhs_subvol)
for subvol_datum, hdf5_datum in zip(subvol_data[3:],
hdf5_data[3:]):
hdf5_datum += subvol_datum
#select the most massive black hole from the last list item
# Also make sure that the total volume takes into account the number of subvolumes read
hdf5_data[1] = hdf5_data[1] * len(subvols)
h0, redshifts = hdf5_data[0], hdf5_data[2]
#for z, m in zip(redshifts, max_smbh):
# print z,m/h0
(mstar_plot, mcold_plot, mhot_plot, meje_plot, mstar_dm_plot,
mcold_dm_plot, mhot_dm_plot, meje_dm_plot, mbar_dm_plot, sfr, sfrd, sfrb,
mstarden, mstarbden_mergers, mstarbden_diskins, sfre, sfreH2, mhrat,
mHI_plot, mH2_plot, mH2den, mdustden, omegaHI, mdustden_mol, mcoldden,
mhotden, mejeden, history_interactions,
mDMden) = prepare_data(hdf5_data, redshifts)
plot_mass_densities(plt, outdir, obsdir, h0, redshifts, mstar_plot,
mcold_plot, mhot_plot, meje_plot, mstarden, mcoldden,
mhotden, mejeden)
plot_baryon_fractions(plt, outdir, redshifts, mstar_dm_plot, mcold_dm_plot,
mhot_dm_plot, meje_dm_plot, mbar_dm_plot)
plot_cosmic_sfr(plt, outdir, obsdir, redshifts, h0, sfr, sfrd, sfrb,
history_interactions, mDMden)
plot_stellar_mass_cosmic_density(plt, outdir, obsdir, redshifts, h0,
mstarden, mstarbden_mergers,
mstarbden_diskins)
plot_sft_efficiency(plt, outdir, redshifts, sfre, sfreH2, mhrat)
plot_mass_cosmic_density(plt, outdir, redshifts, mcold_plot, mHI_plot,
mH2_plot)
plot_omega_h2(plt, outdir, obsdir, redshifts, h0, mH2den)
plot_cosmic_dust(plt, outdir, obsdir, redshifts, h0, mdustden,
mdustden_mol)
plot_omega_HI(plt, outdir, obsdir, redshifts, h0, omegaHI)
def main(modeldir, outdir, redshift_table, subvols):
plt = common.load_matplotlib()
fields = {'galaxies': ('type', 'vvir_hosthalo', 'cooling_rate')}
hdf5_data = common.read_data(modeldir, redshift_table[0], fields, subvols, include_h0_volh=False)
med_tvir = prepare_data(hdf5_data)
plot_cooling_rate(plt, outdir, med_tvir)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
z = [0, 0.5, 1.0, 2.0, 3.0, 4.0]
snapshots = redshift_table[z]
plt = common.load_matplotlib()
mainseqsf = np.zeros(shape=(len(z), 3, len(xmf)))
sigmamainseqsf = np.zeros(shape=(len(z), 7, len(xmf)))
passive_fractions = np.zeros(shape=(len(z), 3, len(xmf2)))
passive_fractions_cens_sats = np.zeros(shape=(len(z), 2, len(xmflr),
len(xmf2)))
hist_ssfr = np.zeros(shape=(len(z), len(ssfrbins)))
fields = {
'galaxies':
('sfr_disk', 'sfr_burst', 'mstars_disk', 'mstars_bulge', 'rstar_disk',
'm_bh', 'matom_disk', 'mmol_disk', 'mgas_disk', 'matom_bulge',
'mmol_bulge', 'mgas_bulge', 'mgas_metals_disk', 'mgas_metals_bulge',
'mstars_metals_disk', 'mstars_metals_bulge', 'type', 'mvir_hosthalo',
'rstar_bulge')
}
for index, snapshot in enumerate(snapshots):
hdf5_data = common.read_data(modeldir, snapshot, fields, subvols)
(mass, slope, offset) = prepare_data(hdf5_data, index, z[index],
mainseqsf, passive_fractions,
hist_ssfr, sigmamainseqsf,
passive_fractions_cens_sats)
h0 = hdf5_data[0]
if index == 0:
(sfr_disk, sfr_burst, mdisk, mbulge) = hdf5_data[2:6]
sfr_seq = np.zeros(shape=(2, len(mdisk)))
ind = np.where((sfr_disk + sfr_burst > 0) & (mdisk + mbulge > 0))
sfr_seq[0, ind] = mass[ind]
sfr_seq[1, ind] = np.log10(
(sfr_disk[ind] + sfr_burst[ind]) / h0 / GyrToYr)
slope_ms_z0 = slope
offset_ms_z0 = offset
#print 'scatter MS'
#for m,a,b,c,d,e,f,g in zip(xmf[:], sigmamainseqsf[index,0,:], sigmamainseqsf[index,1,:], sigmamainseqsf[index,2,:], sigmamainseqsf[index,3,:], sigmamainseqsf[index,4,:], sigmamainseqsf[index,5,:], sigmamainseqsf[index,6,:]):
# print m,a,b,c,d,e,f,g
#print 'passive fractions centrals'
#for m,a,b,c,d,e,f in zip(xmf2[:], passive_fractions_cens_sats[0,0,0,:], passive_fractions_cens_sats[0,0,1,:], passive_fractions_cens_sats[0,0,2,:], passive_fractions_cens_sats[0,0,3,:], passive_fractions_cens_sats[0,0,4,:], passive_fractions_cens_sats[0,0,5,:],):
# print m,a,b,c,d,e,f
#print 'passive fractions satellites'
#for m,a,b,c,d,e,f in zip(xmf2[:], passive_fractions_cens_sats[0,1,0,:], passive_fractions_cens_sats[0,1,1,:], passive_fractions_cens_sats[0,1,2,:], passive_fractions_cens_sats[0,1,3,:], passive_fractions_cens_sats[0,1,4,:], passive_fractions_cens_sats[0,1,5,:],):
# print m,a,b,c,d,e,f
# This should be the same in all HDF5 files
plot_sfr_mstars_z0(plt, outdir, obsdir, h0, sfr_seq, mainseqsf,
sigmamainseqsf, slope_ms_z0, offset_ms_z0)
plot_passive_fraction(plt, outdir, obsdir, passive_fractions, hist_ssfr,
passive_fractions_cens_sats)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
plt = common.load_matplotlib()
fields = {
'galaxies':
('mstars_disk', 'mstars_bulge', 'mstars_burst_mergers',
'mstars_burst_diskinstabilities', 'mstars_bulge_mergers_assembly',
'mstars_bulge_diskins_assembly', 'm_bh', 'rstar_disk', 'rstar_bulge',
'type', 'specific_angular_momentum_disk_star',
'specific_angular_momentum_bulge_star',
'specific_angular_momentum_disk_gas',
'specific_angular_momentum_bulge_gas',
'specific_angular_momentum_disk_gas_atom',
'specific_angular_momentum_disk_gas_mol', 'lambda_subhalo',
'mvir_subhalo', 'mgas_disk', 'mgas_bulge', 'matom_disk', 'mmol_disk',
'matom_bulge', 'mmol_bulge', 'bh_accretion_rate_hh',
'bh_accretion_rate_sb')
}
# Loop over redshift and subvolumes
rcomb = np.zeros(shape=(len(zlist), 3, len(xmf)))
disk_size = np.zeros(shape=(len(zlist), 3, len(xmf)))
bulge_size = np.zeros(shape=(len(zlist), 3, len(xmf)))
bulge_size_mergers = np.zeros(shape=(len(zlist), 3, len(xmf)))
bulge_size_diskins = np.zeros(shape=(len(zlist), 3, len(xmf)))
BH = np.zeros(shape=(len(zlist), 3, len(xmf)))
disk_size_sat = np.zeros(shape=(len(zlist), 3, len(xmf)))
disk_size_cen = np.zeros(shape=(len(zlist), 3, len(xmf)))
BT_fractions = np.zeros(shape=(len(zlist), len(xmf)))
BT_fractions_nodiskins = np.zeros(shape=(len(zlist), len(xmf)))
BT_fractions_centrals = np.zeros(shape=(len(zlist), len(xmf)))
BT_fractions_satellites = np.zeros(shape=(len(zlist), len(xmf)))
disk_vel = np.zeros(shape=(len(zlist), 3, len(xmf)))
bulge_vel = np.zeros(shape=(len(zlist), 3, len(xmf)))
baryonic_TF = np.zeros(shape=(len(zlist), 3, len(xv)))
for index, snapshot in enumerate(redshift_table[zlist]):
hdf5_data = common.read_data(modeldir, snapshot, fields, subvols)
prepare_data(hdf5_data, index, rcomb, disk_size, bulge_size,
bulge_size_mergers, bulge_size_diskins, BH, disk_size_sat,
disk_size_cen, BT_fractions, BT_fractions_nodiskins,
bulge_vel, disk_vel, BT_fractions_centrals,
BT_fractions_satellites, baryonic_TF)
plot_sizes(plt, outdir, obsdir, disk_size_cen, disk_size_sat, bulge_size,
bulge_size_mergers, bulge_size_diskins)
plot_velocities(plt, outdir, disk_vel, bulge_vel, baryonic_TF)
plot_sizes_combined(plt, outdir, rcomb)
plot_bulge_BH(plt, outdir, obsdir, BH)
plot_bt_fractions(plt, outdir, obsdir, BT_fractions,
BT_fractions_nodiskins, BT_fractions_centrals,
BT_fractions_satellites)
def main(model_dir, outdir, redshift_table, subvols, obsdir):
# Loop over redshift and subvolumes
plt = common.load_matplotlib()
fields = {
'galaxies':
('mstars_disk', 'mstars_bulge', 'mvir_hosthalo', 'mvir_subhalo',
'type', 'mean_stellar_age', 'sfr_disk', 'sfr_burst', 'id_galaxy')
}
sfh_fields = {
'bulges_diskins': ('star_formation_rate_histories'),
'bulges_mergers': ('star_formation_rate_histories'),
'disks': ('star_formation_rate_histories')
}
z = (0, 2) #0.5, 1, 1.5, 2, 3)
snapshots = redshift_table[z]
# Create histogram
for index, snapshot in enumerate(snapshots):
hdf5_data = common.read_data(model_dir, snapshot, fields, subvols)
sfh, delta_t, LBT = common.read_sfh(model_dir, snapshot, sfh_fields,
subvols)
seds, ids, nbands = common.read_photometry_data(
model_dir, snapshot, subvols)
(SEDs_dust, SEDs_nodust, total_sfh, sb_sfh, disk_sfh,
gal_props) = prepare_data(hdf5_data, sfh, seds, ids, index, nbands)
h0, volh = hdf5_data[0], hdf5_data[1]
if (index == 0):
SEDs_nodust_z0 = SEDs_nodust
SEDs_dust_z0 = SEDs_dust
total_sfh_z0 = total_sfh
gal_props_z0 = gal_props
LBT_z0 = LBT
plot_individual_seds(plt, outdir, obsdir, h0, SEDs_dust_z0,
SEDs_nodust, total_sfh_z0, gal_props_z0,
LBT_z0)
if (index == 1):
SEDs_dust_z2 = SEDs_dust
total_sfh_z2 = total_sfh
disk_sfh_z2 = disk_sfh
sb_sfh_z2 = sb_sfh
gal_props_z2 = gal_props
LBT_z2 = LBT
plot_individual_seds_z2(plt, outdir, obsdir, h0, SEDs_dust_z2,
total_sfh_z2, disk_sfh_z2, sb_sfh_z2,
gal_props_z2, LBT_z2)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
plt = common.load_matplotlib()
fields = {
'galaxies':
('mstars_disk', 'mstars_bulge', 'mstars_burst_mergers',
'mstars_burst_diskinstabilities', 'mstars_bulge_mergers_assembly',
'mstars_bulge_diskins_assembly', 'm_bh', 'rstar_disk', 'rstar_bulge',
'type', 'specific_angular_momentum_disk_star',
'specific_angular_momentum_bulge_star',
'specific_angular_momentum_disk_gas',
'specific_angular_momentum_bulge_gas',
'specific_angular_momentum_disk_gas_atom',
'specific_angular_momentum_disk_gas_mol', 'lambda_subhalo',
'mvir_subhalo', 'mvir_hosthalo', 'matom_disk', 'mmol_disk',
'mgas_disk', 'matom_bulge', 'mmol_bulge', 'mgas_bulge', 'sfr_disk',
'sfr_burst', 'vvir_hosthalo', 'rgas_disk', 'rgas_bulge')
}
# Loop over redshift and subvolumes
sam_vs_sam_halo_disk = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
sam_vs_sam_halo_gal = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
sam_vs_sam_halo_disk_gas = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
sam_vs_sam_halo_bar = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
m_vs_m_halo_disk = np.zeros(shape=(len(zlist), 3, len(xmf), 2))
m_vs_m_halo_gal = np.zeros(shape=(len(zlist), 3, len(xmf), 2))
m_vs_m_halo_disk_gas = np.zeros(shape=(len(zlist), 3, len(xmf), 2))
m_vs_m_halo_bar = np.zeros(shape=(len(zlist), 3, len(xmf), 2))
r_vs_r_halo_disk = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
r_vs_r_halo_gal = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
r_vs_r_halo_disk_gas = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
r_vs_r_halo_bar = np.zeros(shape=(len(zlist), 3, len(xlf), 2))
for index, snapshot in enumerate(redshift_table[zlist]):
hdf5_data = common.read_data(modeldir, snapshot, fields, subvols)
(lh, lj, lm, bt, ms, ssfr) = prepare_data(
hdf5_data, index, sam_vs_sam_halo_disk, sam_vs_sam_halo_gal,
sam_vs_sam_halo_disk_gas, sam_vs_sam_halo_bar, m_vs_m_halo_disk,
m_vs_m_halo_gal, m_vs_m_halo_disk_gas, m_vs_m_halo_bar,
r_vs_r_halo_disk, r_vs_r_halo_gal, r_vs_r_halo_disk_gas,
r_vs_r_halo_bar)
plot_specific_am_ratio(plt, outdir, obsdir, sam_vs_sam_halo_disk,
sam_vs_sam_halo_gal, sam_vs_sam_halo_disk_gas,
sam_vs_sam_halo_bar, m_vs_m_halo_disk,
m_vs_m_halo_gal, m_vs_m_halo_disk_gas,
m_vs_m_halo_bar, r_vs_r_halo_disk, r_vs_r_halo_gal,
r_vs_r_halo_disk_gas, r_vs_r_halo_bar)
def main(model_dir, output_dir, redshift_table, subvols, obs_dir):
plt = common.load_matplotlib()
fields = {'galaxies': ('type', 'mstars_disk', 'mstars_bulge',
'rstar_disk', 'm_bh', 'matom_disk', 'mmol_disk', 'mgas_disk',
'matom_bulge', 'mmol_bulge', 'mgas_bulge', 'mvir_hosthalo',
'id_halo_tree', 'mhot', 'position_x', 'position_y', 'position_z',
'velocity_x', 'velocity_y', 'velocity_z', 'vvir_hosthalo')}
hdf5_data = common.read_data(model_dir, redshift_table[0], fields, subvols)
(mHI_halos_stacking, sats_vproj, sats_rproj, sats_type) = prepare_data(hdf5_data)
plot_HI_gas_fraction_groups(plt, output_dir, obs_dir, mHI_halos_stacking)
plot_caustic_halos(plt, output_dir, sats_vproj, sats_rproj, sats_type)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
plt = common.load_matplotlib()
fields = {'galaxies': ('mstars_disk', 'mstars_bulge', 'mstars_burst_mergers', 'mstars_burst_diskinstabilities',
'mstars_bulge_mergers_assembly', 'mstars_bulge_diskins_assembly', 'm_bh', 'rstar_disk', 'rstar_bulge', 'type',
'specific_angular_momentum_disk_star', 'specific_angular_momentum_bulge_star',
'specific_angular_momentum_disk_gas', 'specific_angular_momentum_bulge_gas',
'specific_angular_momentum_disk_gas_atom', 'specific_angular_momentum_disk_gas_mol',
'lambda_subhalo', 'mvir_subhalo', 'mvir_hosthalo', 'matom_disk', 'mmol_disk', 'mgas_disk',
'matom_bulge', 'mmol_bulge', 'mgas_bulge','sfr_disk', 'sfr_burst','vmax_subhalo')}
# Loop over redshift and subvolumes
sam_stars_disk = np.zeros(shape = (len(zlist), 3, len(xmf),2))
sam_gas_disk_atom = np.zeros(shape = (len(zlist), 3, len(xmf),2))
sam_gas_disk_mol = np.zeros(shape = (len(zlist), 3, len(xmf),2))
sam_bar = np.zeros(shape = (len(zlist), 3, len(xmf),2))
sam_halo = np.zeros(shape = (len(zlist), 3, len(xmf), 2))
sam_stars = np.zeros(shape = (len(zlist), 3, len(xmf), 2))
sam_ratio_halo_disk = np.zeros(shape = (len(zlist), 3, len(xmfh), 2))
sam_ratio_halo_gal = np.zeros(shape = (len(zlist), 3, len(xmfh), 2))
sam_ratio_halo_disk_gas = np.zeros(shape = (len(zlist), 3, len(xmfh), 2))
vmax_halo_gal = np.zeros(shape = (len(zlist), 3, len(xmfh), 2))
sam_vs_sam_halo_disk = np.zeros(shape = (len(zlist), 3, len(xlf), 2))
sam_vs_sam_halo_gal = np.zeros(shape = (len(zlist), 3, len(xlf), 2))
sam_vs_sam_halo_disk_gas = np.zeros(shape = (len(zlist), 3, len(xlf), 2))
disk_size_sat = np.zeros(shape = (len(zlist), 3, len(xmf)))
disk_size_cen = np.zeros(shape = (len(zlist), 3, len(xmf)))
bulge_size = np.zeros(shape = (len(zlist), 3, len(xmf)))
for index, snapshot in enumerate(redshift_table[zlist]):
hdf5_data = common.read_data(modeldir, snapshot, fields, subvols)
(lh, lj, lm, bt, ms, ssfr) = prepare_data(hdf5_data, index, sam_stars_disk, sam_gas_disk_atom, sam_gas_disk_mol, sam_halo, sam_ratio_halo_disk,
sam_ratio_halo_gal, sam_ratio_halo_disk_gas, disk_size_sat, disk_size_cen, bulge_size, sam_vs_sam_halo_disk, sam_vs_sam_halo_gal,
sam_vs_sam_halo_disk_gas, sam_bar, sam_stars, vmax_halo_gal)
if(index == 0):
lambdaH = lh
lambda_jiang = lj
lambda_mass = lm
BT_ratio = bt
stellar_mass = ms
ssfr_z0 = ssfr
plot_specific_am(plt, outdir, obsdir, sam_stars_disk, sam_gas_disk_atom, sam_gas_disk_mol, sam_halo, sam_bar, sam_stars)
plot_specific_am_ratio(plt, outdir, obsdir, sam_ratio_halo_disk, sam_ratio_halo_gal, sam_ratio_halo_disk_gas,
sam_vs_sam_halo_disk, sam_vs_sam_halo_gal, sam_vs_sam_halo_disk_gas)
plot_lambda(plt, outdir, obsdir, lambdaH, lambda_jiang, lambda_mass, BT_ratio, stellar_mass, ssfr_z0)
plot_sizes(plt, outdir, obsdir, disk_size_cen, disk_size_sat, bulge_size, vmax_halo_gal)
def main():
model_dir = '/fred/oz009/clagos/vr-testing-outputs/hydro/'
output_dir = '/fred/oz009/clagos/vr-testing-outputs/Plots/hydro/'
obs_dir = '/home/clagos/scm/analysis/VR'
subvols = [0,1,2]
snap = [28, 19, 15]
vol = 25.0**3.0 #Mpc
vol_eagle = 25.0**3.0
name_file = 'fourth-try-6dfofsubhalos'#:'third-try'
plt = common.load_matplotlib()
fields = ['Mass_BN98','Mass_FOF','Mass_200crit','Mass_200mean','Mass_tot','R_200crit','R_200mean','hostHaloID','ID','Vmax','Rmax','Xcmbp','Ycmbp','Zcmbp','cNFW','lambda_B','npart']
# Create histogram
hist = np.zeros(shape = (2,len(snap),len(mbins)))
histsh = np.zeros(shape = (len(snap),len(mbins)))
histvmax = np.zeros(shape = (2,len(snap),len(vbins)))
R200med = np.zeros(shape = (2,len(snap),3,len(mbins)))
rvmaxmed = np.zeros(shape = (2,len(snap),3,len(vbins)))
cmed = np.zeros(shape = (2,len(snap),3,len(mbins)))
lambdamed = np.zeros(shape = (2,len(snap),3,len(mbins)))
for j in range(0,len(snap)):
hdf5_data = common.read_data(model_dir, fields, snap[j], subvols, name_file)
prepare_data(hdf5_data, j, hist, histsh, R200med, rvmaxmed, histvmax, cmed, lambdamed)
# Take logs
ind = np.where(hist > 0.)
hist[ind] = np.log10(hist[ind]/vol/dm)
ind = np.where(histsh > 0.)
histsh[ind] = np.log10(histsh[ind]/vol/dm)
ind = np.where(histvmax > 0.)
histvmax[ind] = np.log10(histvmax[ind]/vol/dm)
plot_halomf_z(plt, output_dir+name_file+'/', obs_dir, snap, vol_eagle, hist, histsh)
plot_r200_z(plt, output_dir+name_file+'/', R200med, snap)
plot_rivmax_vmax_z(plt, output_dir+name_file+'/', rvmaxmed, histvmax, snap, vol_eagle)
plot_con_lambda(plt, output_dir+name_file+'/', cmed, lambdamed, snap)
def main(modeldir, outdir, redshift_table, subvols):
plt = common.load_matplotlib()
fields = {
'galaxies':
('mstars_disk', 'mstars_bulge', 'm_bh', 'mgas_disk', 'mgas_bulge',
'mhot', 'mreheated', 'mvir_hosthalo', 'type')
}
zlist = (0, 0.5, 1, 2, 3, 4)
snapshots = redshift_table[zlist]
massgal = np.zeros(shape=(len(zlist), 3, len(xmf)))
massbar = np.zeros(shape=(len(zlist), 3, len(xmf)))
massbar_inside = np.zeros(shape=(len(zlist), 3, len(xmf)))
for idx, snapshot in enumerate(snapshots):
hdf5_data = common.read_data(modeldir, snapshot, fields, subvols)
prepare_data(hdf5_data, idx, massgal, massbar, massbar_inside)
plot_SMHM_z(plt, outdir, zlist, massgal)
plot_BMHM_z(plt, outdir, massbar, massbar_inside)
def main(model_dir, outdir, redshift_table, subvols, obsdir):
# Loop over redshift and subvolumes
plt = common.load_matplotlib()
fields = {
'galaxies': ('mstars_disk', 'mstars_bulge', 'mvir_hosthalo',
'mvir_subhalo', 'type')
}
z = (0, 0.5, 1, 2)
snapshots = redshift_table[z]
# Create histogram
hist = np.zeros(shape=(len(z), len(mbins)))
histsh = np.zeros(shape=(len(z), len(mbins)))
plotz = np.empty(shape=(len(z)), dtype=np.bool_)
for index, snapshot in enumerate(snapshots):
hdf5_data = common.read_data(model_dir, snapshot, fields, subvols)
prepare_data(hdf5_data, hist, histsh, index)
h0, volh = hdf5_data[0], hdf5_data[1]
if (volh > 0.):
vol = volh / pow(h0, 3.) # In Mpc^3
hist[index, :] = hist[index, :] / vol / dm
histsh[index, :] = histsh[index, :] / vol / dm
plotz[index] = True
else:
plotz[index] = False
# Take logs
ind = np.where(hist > 0.)
hist[ind] = np.log10(hist[ind])
ind = np.where(histsh > 0.)
histsh[ind] = np.log10(histsh[ind])
plot_halomf_z(plt, outdir, obsdir, z, h0, hist, histsh, plotz)
def main(modeldir, outdir, redshift_table, subvols, obsdir):
plt = common.load_matplotlib()
fields = {
'galaxies':
('type', 'mstars_disk', 'mstars_bulge', 'rstar_disk', 'm_bh',
'matom_disk', 'mmol_disk', 'mgas_disk', 'matom_bulge', 'mmol_bulge',
'mgas_bulge', 'mvir_hosthalo')
}
hdf5_data = common.read_data(modeldir, redshift_table[0], fields, subvols)
(hist_smf, hist_smf_sat, hist_bmf, hist_bmf_sat, mHIms, mHIms_true,
mHImhalo, mHImhalo_true, ETGsmhalo, LTGsmhalo,
hist_himf) = prepare_data(hdf5_data)
plot_smf_resolve(plt, outdir, obsdir, hist_smf, hist_smf_sat)
plot_bmf_resolve(plt, outdir, obsdir, hist_bmf, hist_bmf_sat)
plot_mHI_mstar_resolve(plt, outdir, obsdir, mHIms, mHIms_true)
plot_mHI_mhalo_resolve(plt, outdir, obsdir, mHImhalo, mHImhalo_true)
plot_bt_resolve(plt, outdir, obsdir, ETGsmhalo, LTGsmhalo)
plot_bmf_resolve_baryon_components(plt, outdir, obsdir, hist_bmf, hist_smf,
hist_himf)
def main():
model_dir = '/fred/oz009/clagos/vr-testing-outputs/hydro/'
output_dir = '/fred/oz009/clagos/vr-testing-outputs/Plots/hydro/'
obs_dir = '/home/clagos/scm/analysis/VR'
subvols = [0, 1, 2]
snap = [28, 19, 15]
vol = 25.0**3.0 #Mpc
vol_eagle = 25.0**3.0
name_file = 'fourth-try-6dfofsubhalos' #:'third-try'
plt = common.load_matplotlib()
fields = [
'Aperture_mass_star_30_kpc', 'M_star', 'Aperture_mass_gas_30_kpc',
'R_HalfMass_star', 'SFR_gas', 'Aperture_mass_30_kpc', 'M_bh',
'Aperture_rhalfmass_star_30_kpc', 'Aperture_veldisp_star_30_kpc',
'Zmet_gas', 'Zmet_gas_sf', 'Zmet_star', 'tage_star',
'Aperture_SFR_gas_30_kpc',
'Projected_aperture_1_rhalfmass_star_30_kpc',
'Projected_aperture_2_rhalfmass_star_30_kpc',
'Projected_aperture_3_rhalfmass_star_30_kpc', 'Zmet_gas_nsf'
]
# Create histogram for mass functions
histmtot = np.zeros(shape=(len(snap), len(mbins)))
histm30 = np.zeros(shape=(len(snap), len(mbins)))
histmgas = np.zeros(shape=(len(snap), len(mbins)))
histmall = np.zeros(shape=(len(snap), len(mbins)))
# create matrices for several scaling relations
SFRMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
SFRMstar30 = np.zeros(shape=(len(snap), 3, len(mbins)))
R50Mstar = np.zeros(shape=(len(snap), 3, len(mbins)))
SigmaMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
MBHMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
R50Mstar30 = np.zeros(shape=(len(snap), 3, len(mbins)))
R50pMstar30 = np.zeros(shape=(len(snap), 3, len(mbins)))
SigmaMstar30 = np.zeros(shape=(len(snap), 3, len(mbins)))
ZstarMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
AgeSMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
ZSFMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
ZNSFMstar = np.zeros(shape=(len(snap), 3, len(mbins)))
for j in range(0, len(snap)):
hdf5_data = common.read_data(model_dir, fields, snap[j], subvols,
name_file)
prepare_data(hdf5_data, j, histmtot, histm30, histmgas, SFRMstar,
R50Mstar, SigmaMstar, histmall, MBHMstar, R50Mstar30,
SigmaMstar30, ZstarMstar, ZSFMstar, AgeSMstar, SFRMstar30,
R50pMstar30, ZNSFMstar)
# Take logs for mass functions
ind = np.where(histmtot > 0.)
histmtot[ind] = np.log10(histmtot[ind] / vol / dm)
ind = np.where(histm30 > 0.)
histm30[ind] = np.log10(histm30[ind] / vol / dm)
ind = np.where(histmgas > 0.)
histmgas[ind] = np.log10(histmgas[ind] / vol / dm)
ind = np.where(histmall > 0.)
histmall[ind] = np.log10(histmall[ind] / vol / dm)
#mass funcion plots
plot_mf_z(plt, output_dir + name_file + '/', snap, vol_eagle, histmtot,
histm30, histmgas, histmall)
#scaling relation plots
plot_scaling_z(plt, output_dir + name_file + '/', snap, SFRMstar, R50Mstar,
R50Mstar30, MBHMstar, SigmaMstar30, ZstarMstar, ZSFMstar,
AgeSMstar, SFRMstar30, R50pMstar30, ZNSFMstar)
