def update_plot(i):
sim = simlist[i]
host, subs = load_elvis(sim=sim, processed=True)
# subs = subs[subs.nadler2018 > 0.5]
bind = -subs.pot_mltr.values - 0.5*(subs.v_r.values**2 + subs.v_t.values**2)
z = WMAP7.lookback_time(1/subs.a_acc.values - 1)[bind>0].value
r = subs.r[bind>0]
bind = bind[bind>0]
# calculate linear regression
infallcut = z > 2
bindcut = bind > 0
bind_ = bind[infallcut & bindcut]
infall_ = z[infallcut & bindcut]
slope, intercept, r_value, p_value, std_err = linregress(infall_, np.log10(bind_))
# update plot
offset = [[infall, np.log10(E)] for E,infall in zip(bind,z)]
scat.set_offsets(offset) # positions
scat.set_array(r) # colors
line.set_data(z[bindcut], intercept + slope*z[bindcut])
sim_text.set_text(sim)
sortprop_text.set_text("Mvir = "'%.2E' % sortprop[i])
return scat,sim_text,sortprop_text,line
def main(datadir='/data',
resultsdirs=['results'],
plotsdir='plots',
labels=['results'],
counts_file='galaxy_counts.txt',
snapnum_file='snapnum_redshift.csv',
dwarf_limit=0.05,
plotlabel='',
evolution=False):
"""
Plot results of Millennium Simulation compact group analysis
"""
if not os.path.isdir(datadir):
raise ValueError("{0} not found!".format(datadir))
for resultsdir in resultsdirs:
if not os.path.isdir(resultsdir):
raise ValueError("{0} not found!".format(resultsdir))
if not os.path.isdir(plotsdir):
os.mkdir(plotsdir)
if not os.path.exists(snapnum_file):
raise ValueError("{0} not found!".format(snapnum_file))
#
# Read snapnum file to convert from snapnum to redshift
#
snap_to_z = pandas.read_csv(snapnum_file,
header=0,
comment='#',
skip_blank_lines=True,
index_col=0,
skipinitialspace=True)
#
# Determine number of snapnums we have in datadir
#
if evolution:
data_snapnum_dirs = np.arange(64)
else:
data_snapnum_dirs = np.sort(
glob.glob(os.path.join(datadir, 'snapnum_*')))
print("Found {0} snapnum directories in {1}.".format(
len(data_snapnum_dirs), datadir))
if len(data_snapnum_dirs) == 0 and not os.path.exists(counts_file):
return
#
# Count the number and standard devation of non-dwarf galaxies in raw data files
#
if os.path.exists(counts_file):
print("Found {0}".format(counts_file))
else:
generate_snapnum_counts(datadir, data_snapnum_dirs, data_file,
dwarf_limit, counts_file)
#
# Read the galaxy counts file
#
galaxy_counts = pandas.read_csv(counts_file,
header=0,
comment='#',
skip_blank_lines=True,
index_col=0,
skipinitialspace=True)
#
# Get results from each resultsdir
#
if evolution:
num_members, num_members_std = data_read_in_evolution(
resultsdirs, data_snapnum_dirs)
else:
members, groups, num_non_dwarf_members, non_dwarf_members, num_members_std, num_non_dwarf_members_std, num_groups_std = data_read_in(
resultsdirs, data_snapnum_dirs, datadir)
#
# Convert snapnums to redshifts and to lookback times
#
snapnums = [snapnum for snapnum in range(len(data_snapnum_dirs))]
redshifts = np.array([snap_to_z['Z'][snapnum] for snapnum in snapnums])
lbtimes = WMAP7.lookback_time(redshifts).value
plot_redshifts = [0.0, 0.1, 0.2, 0.3, 0.5, 1, 1.5, 2, 3, 5, 10]
plot_lookback_times = WMAP7.lookback_time(plot_redshifts).value
#
colors = ['#d73027', '#fc8d59', '#fee090']
symbols = ['o', 's', '^', '*', 'p']
#
# Make Evolution Plots
#
if evolution:
plot_evolution(plotlabel, galaxy_counts, num_members, num_members_std,
resultsdirs, colors, symbols, lbtimes,
plot_lookback_times, plot_redshifts, plotsdir, labels)
return
#
# Plot total number of compact groups vs. redshift
#
figname = os.path.join(plotsdir, '{0}_num_groups.eps'.format(plotlabel))
num_cgs = np.array([[len(df) for df in groups[result_ind]]
for result_ind in range(len(resultsdirs))])
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
for ind, (num_cg, num_cg_std) in enumerate(zip(num_cgs, num_groups_std)):
ax.plot(lbtimes,
num_cg,
color=colors[ind],
marker=symbols[ind],
label=labels[ind])
ax.fill_between(lbtimes,
num_cg - num_cg_std,
num_cg + num_cg_std,
facecolor=colors[ind],
edgecolor="none",
alpha=0.6)
ax.set_xlabel('Lookback Time (Gyr)')
ax.set_ylabel('Number of Compact Groups')
ax.set_yscale('log')
ax.set_xlim(0, WMAP7.age(0).to('Gyr').value)
ax.set_ylim(1.e2, 1.e5)
ax.legend(loc='best', fontsize=12, numpoints=1)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(plot_lookback_times)
ax2.set_xticklabels(plot_redshifts)
ax2.set_xlabel('Redshift')
ax2.grid(False)
plt.savefig(figname)
plt.close()
#
# Plot total number of galaxies in compact groups vs. redshift
#
figname = os.path.join(plotsdir, '{0}_num_members.eps'.format(plotlabel))
num_galaxies = np.array([[len(df) for df in non_dwarf_members[result_ind]]
for result_ind in range(len(resultsdirs))])
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
for ind, (num_galaxy, num_galaxy_std) in enumerate(
zip(num_galaxies, num_non_dwarf_members_std)):
ax.plot(lbtimes,
num_galaxy,
color=colors[ind],
marker=symbols[ind],
label=labels[ind])
ax.fill_between(lbtimes,
num_galaxy - num_galaxy_std,
num_galaxy + num_galaxy_std,
facecolor=colors[ind],
edgecolor='none',
alpha=0.6)
ax.set_xlabel('Lookback Time (Gyr)')
ax.set_ylabel('Number of non-dwarf Compact Group Members')
ax.set_yscale('log')
ax.legend(loc='best', fontsize=12, numpoints=1)
ax.set_xlim(0, WMAP7.age(0).to('Gyr').value)
ax.set_ylim(1.e3, 5.e5)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(plot_lookback_times)
ax2.set_xticklabels(plot_redshifts)
ax2.set_xlabel('Redshift')
ax2.grid(False)
plt.savefig(figname)
plt.close()
#
# Plot fractional number of galaxies in compact groups vs. redshift
#
figname = os.path.join(plotsdir, '{0}_frac_galaxies.eps'.format(plotlabel))
frac_galaxies = np.array([[
len(df) / galaxy_counts['num_galaxies'][snapnum]
for snapnum, df in enumerate(non_dwarf_members[result_ind])
] for result_ind in range(len(resultsdirs))])
frac_galaxies_std = frac_galaxies * np.sqrt(
np.array([[(num_galaxy_std / len(df))**2. +
(galaxy_counts['std_galaxies'][snapnum] /
galaxy_counts['num_galaxies'][snapnum])**2.
for snapnum, (df, num_galaxy_std) in enumerate(
zip(non_dwarf_members[result_ind],
num_non_dwarf_members_std[result_ind]))]
for result_ind in range(len(resultsdirs))]))
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
for ind, (frac_galaxy, frac_galaxy_std) in enumerate(
zip(frac_galaxies, frac_galaxies_std)):
ax.plot(lbtimes,
100. * frac_galaxy,
color=colors[ind],
marker=symbols[ind],
label=labels[ind])
ax.fill_between(lbtimes,
100. * (frac_galaxy - frac_galaxy_std),
100. * (frac_galaxy + frac_galaxy_std),
facecolor=colors[ind],
edgecolor='none',
alpha=0.6)
ax.set_xlabel('Lookback Time (Gyr)')
ax.set_ylabel('Percent of non-dwarf Galaxies in Compact Groups')
#ax.set_yscale('log')
ax.legend(loc='best', fontsize=12, numpoints=1)
ax.set_xlim(0, WMAP7.age(0).to('Gyr').value)
ax.set_ylim(0., 1.5)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(plot_lookback_times)
ax2.set_xticklabels(plot_redshifts)
ax2.set_xlabel('Redshift')
ax2.grid(False)
plt.savefig(figname)
plt.close()
return
#
# Now with redshift
#
#
# Plot total number of compact groups vs. redshift
#
num_cgs = [len(df) for df in groups]
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
ax.plot(redshifts, num_cgs, 'k-')
ax.plot(redshifts, num_cgs, 'ko')
ax.set_xlabel('Redshift')
ax.set_ylabel('Number of Compact Groups')
ax.set_yscale('log')
ax.set_xlim(0, 12)
ax.set_ylim(1.e2, 1.e5)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(np.arange(13))
ax2.set_xticklabels(WMAP7.lookback_time(np.arange(13)).value)
ax2.set_xlabel('Lookback time (Gyr)')
ax2.grid(False)
plt.savefig('num_groups_z.eps')
plt.close()
#
# Plot total number of galaxies in compact groups vs. redshift
#
num_galaxies = [len(df) for df in non_dwarf_members]
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
ax.plot(redshifts, num_galaxies, 'k-')
ax.plot(redshifts, num_galaxies, 'ko')
ax.set_xlabel('Redshift')
ax.set_ylabel('Number of non-dwarf Compact Group Members')
ax.set_yscale('log')
ax.set_xlim(0, 12)
ax.set_ylim(1.e2, 1.e5)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(np.arange(13))
ax2.set_xticklabels(WMAP7.lookback_time(np.arange(13)).value)
ax2.set_xlabel('Lookback time (Gyr)')
ax2.grid(False)
plt.savefig('num_members_z.eps')
plt.close()
#
# Plot fractional number of galaxies in compact groups vs. redshift
#
frac_galaxies = [
len(df) / galaxy_counts['num_galaxies'][snapnum]
for snapnum, df in enumerate(non_dwarf_members)
]
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twiny()
ax.plot(redshifts, frac_galaxies, 'k-')
ax.plot(redshifts, frac_galaxies, 'ko')
ax.set_xlabel('Redshift')
ax.set_ylabel('Fracion of non-dwarf Galaxies in Compact Groups')
ax.set_xlim(0, 12)
ax.set_ylim(1.e-4, 1.e-2)
ax2.set_xlim(ax.get_xlim())
ax2.set_xticks(np.arange(13))
ax2.set_xticklabels(WMAP7.lookback_time(np.arange(13)).value)
ax2.set_xlabel('Lookback time (Gyr)')
ax2.grid(False)
plt.savefig('frac_galaxies_z.eps')
plt.close()
return
simlist.append(sim)
simlist.append('vl2')
slopes, b, Mvir, c, apeak, error, mass_change, Mpeak, Vmax = [[]
for i in range(9)
]
for sim in simlist:
if sim == 'vl2':
halos, subs = load_vl2(scale=1.0, processed=True)
subs = subs[subs.Vmax > 8]
else:
halos, subs = load_elvis(sim=sim, processed=True)
# subs = subs[subs.nadler2018 > 0.5]
pot = subs.pot_mltr
bind = -pot - 0.5 * (subs.v_r**2 + subs.v_t**2)
infall = WMAP7.lookback_time(1 / subs.a_acc - 1).value
infallcut = infall > 2
bindcut = bind > 0
bind_ = bind[infallcut & bindcut]
infall_ = infall[infallcut & bindcut]
# linear regression
slope, intercept, r_value = linregress(infall_, np.log10(bind_))[:3]
pred = intercept + slope * infall_
# save the params
slopes.append(slope)
error.append(MSE(np.log10(bind_), pred))
b.append(intercept)
if sim == 'vl2':
Mvir.append(1.93596e+12)
if sim[0] == 'i':
simlist.append(sim)
# grab ALL subhalos that are desired
r, v_r, v_t, a_acc = [np.array([]) for i in range(4)]
for sim in simlist:
subs = pd.read_pickle('derived_props/'+sim)
r = np.append(r, subs.r.values)
v_r = np.append(v_r, subs.v_r.values)
v_t = np.append(v_t, subs.v_t.values)
a_acc = np.append(a_acc, subs.a_acc.values)
dict = {'r': r, 'v_r': v_r, 'v_t': v_t, 'a_acc': a_acc}
subs = pd.DataFrame(dict)
# generate PDFs for each satellite
for name, sat in sats.iterrows():
rconstraint = (sat.r_lower < subs.r) & (subs.r < sat.r_upper)
vconstraint = (subs.v_r < sat.v_r_upper) & (sat.v_r_lower < subs.v_r)
subset = subs[rconstraint & vconstraint]
subset2 = subset[(sat.v_t_lower < subset.v_t) & (subset.v_t < sat.v_t_upper)]
if len(subset) == 0 or len(subset2) == 0:
continue
plt.hist(WMAP7.lookback_time(1/subset.a_acc - 1).value)
plt.hist(WMAP7.lookback_time(1/subset2.a_acc - 1).value)
plt.title(name)
plt.xlabel('Infall Time [Gyr]')
plt.savefig('figures/infall_pdfs/'+name+'.png',bbox_inches='tight')
plt.close()
bind, z, r, v_r = [np.array([]) for i in range(4)]
for sim in list_of_sims('elvis'):
if sim[0] != 'i' or 'HiRes' in sim:
continue
try:
halos, subs = load_elvis(sim=sim, processed=True)
subs = subs[subs.nadler2018 > 0.5]
pot = subs.pot_mltr
except AttributeError:
print(sim + " not included")
continue
bind_sim = -pot - 0.5 * (subs.v_r.values**2 + subs.v_t.values**2)
bind = np.append(bind, bind_sim)
z = np.append(z, WMAP7.lookback_time(1 / subs.a_acc.values - 1))
r = np.append(r, subs.r)
v_r = np.append(v_r, subs.v_r)
"""
plt.scatter(WMAP7.lookback_time(1/subs.a_acc.values - 1)[bind_sim>0], np.log10(bind_sim[bind_sim>0]), s=2.0, c=subs.r[bind_sim>0], cmap='plasma', vmin=0.0, vmax=max_r)
plt.colorbar().set_label(r'Galactocentric Radius [$kpc$]')
plt.xlim(0.0, WMAP7.lookback_time(np.inf).value)
plt.ylim(3.4,5.2)
plt.yticks([3.5,4.0,4.5,5.0])
plt.xlabel(r'Infall time [$Gyr$]')
plt.ylabel(r'log(Binding Energy) [$km^2\ s^{-2}$]');
plt.savefig('figures/eachvolume/rocha_fig1_'+sim+'.png', bbox_inches='tight')
plt.close()
plt.scatter(subs.r[bind_sim>0], subs.v_r[bind_sim>0], s=2.0, c=WMAP7.lookback_time(1/subs.a_acc.values - 1)[bind_sim>0], cmap='plasma')
plt.colorbar().set_label(r'Infall time [$Gyr$]')
F.DLA = d.zabs, 10**d.NH2 / (10**d.NHI + 2*10**d.NH2)
pl.rc('font', size=14)
#pl.rc('legend', fontsize=12)
fig = pl.figure(figsize=(3.5, 3.7))
fig.subplots_adjust(top=0.88, right=0.97, left=0.165, bottom=0.12)
ax = pl.gca()
fvals = np.array(list(np.log10(F.MWplane[1])) + list(np.log10(F.SMC[1])) + \
list(np.log10(F.LMC[1])))
f0,f1,f2 = np.percentile(fvals[fvals > -20], [10,50, 90])
ax.plot(0, f1, 'r^', ms=8, mew=0, label='$\mathrm{Local\ group}$',)
ax.errorbar(0, f1, yerr=np.transpose([(f1-f0, f2-f1)]),
ecolor='r', capsize=3, mew=1, fmt=None)
ax.plot(WMAP7.lookback_time(F.DLA[0]),np.log10(F.DLA[1]), 'ok', ms=5, mfc='none', mew=0.5, label='$z>1.5\ \ \mathrm{DLA/sub\,DLA}$')
x = WMAP7.lookback_time(F.C[0])
y = np.log10(F.C[1])
ax.scatter(x,y, marker='o', c='k',s=70, zorder=10,linewidths=0.5,label='$\mathrm{This\ paper}$')
plot([x,x], [y-0.36, y+0.36], 'k')
ax.set_xlabel('$\mathrm{Lookback\ time\ (Gyr)}$')
ax.set_ylabel('$\log_{10}\ f_{\mathrm{H}_2}$')
ax.set_xlim(-0.9, 13.5)
ax.set_ylim(-8.99, 0.49)
leg = ax.legend(loc='lower left', frameon=0, scatterpoints=1)
leg.get_frame().set_lw(0.5)
ax1 = pl.twiny(ax)
