def plot_HI_gas_fraction_groups(plt, output_dir, obs_dir, mHI_halos_stacking):
###################################
# Plots global mass densities
fig = plt.figure(figsize=(5,4.5))
xtit="$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot}\,h^{-1})$"
ytit="$\\rm log_{10}(M_{\\rm HI}/M_{\\rm halo})$"
ax = fig.add_subplot(111)
plt.subplots_adjust(bottom=0.15, left=0.15)
prepare_ax(ax, 10, 15, -5, -0.5, xtit, ytit)
#Predicted SMHM
ind = np.where((mHI_halos_stacking > 0) & (xmf > 10.3))
xplot = xmf[ind]
yplot = mHI_halos_stacking[ind]-xmf[ind]
for i,j in zip (xplot,yplot):
print i,j
ax.plot(xplot,yplot, color='k', linestyle='solid', label='Shark')
common.prepare_legend(ax, ['k'])
common.savefig(output_dir, fig, "HI_groups_stacking.pdf")
def conduct_bad_and_good(x, data, filename):
"""Study BadAndGoodLS algorithm."""
r_1 = BadAndGoodLS(x, data, beta=0.01, gamma=40)
r_1.fit()
r_2 = BadAndGoodLS(x, data, beta=0.01, gamma=100)
r_2.fit()
r_3 = BadAndGoodLS(x, data, beta=0.01, gamma=200)
r_3.fit()
fig, ax = plt.subplots(1, 1, figsize=FIG_SIZE)
ax.plot(x, data, 'o', label='Data')
ax.plot(x, r_1.result, '-', label=r_1.name)
ax.plot(x, r_2.result, '--', label=r_2.name)
ax.plot(x, r_3.result, '-.', label=r_3.name)
ax.legend(loc='best')
ax.set_xlabel(r'$x$')
ax.set_ylabel('Observations and fit')
fig.tight_layout(pad=0.1)
savefig(fig, filename)
r_1.print_result()
r_2.print_result()
r_3.print_result()
def plot_cooling_rate(plt, outdir, med_tvir):
fig = plt.figure(figsize=(5,5))
xmin, xmax, ymin, ymax = -1.8, 1.2, -1, 6
ax = fig.add_subplot(111)
xtit="$\\rm log_{10}(T_{\\rm vir}/\\rm keV)$"
ytit="$\\rm log_{10}(L_{\\rm cool}/ 10^{40}\\rm erg\,s^{-1})$"
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
xleg= xmax - 0.2 * (xmax - xmin)
yleg= ymax - 0.1 * (ymax - ymin)
ax.text(xleg, yleg, 'z=0')
ind = np.where(med_tvir[0, :] != 0)
xplot = tfunc[ind]
yplot = med_tvir[0, ind]
errdn = med_tvir[1, ind]
errup = med_tvir[2, ind]
ax.errorbar(xplot,yplot[0],color='k', label="SHArk")
ax.errorbar(xplot,yplot[0],yerr=[errdn[0],errup[0]], ls='None', mfc='None', ecolor = 'k', mec='k',marker='+',markersize=2)
ax.plot(tfunc, 3.0 * tfunc + 1.9, 'r', linestyle='dashed', label='Anderson+15')
common.prepare_legend(ax, ['r','k'], loc=2)
common.savefig(outdir, fig, 'cooling_rate.pdf')
def plot_sft_efficiency(plt, outdir, redshifts, sfre, sfreH2, mhrat):
fig = plt.figure(figsize=(9.5, 11))
xmin, xmax, ymin, ymax = 0, 10, -6, 0
# panel 1
ax = fig.add_subplot(311)
plt.subplots_adjust(bottom=0.15, left=0.15)
xtit = "$\\rm redshift$"
ytit = "$\\rm log_{10}(SFR/M_{\\rm cold} Gyr^{-1})$"
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
#note that only h^2 is needed because the volume provides h^3, and the SFR h^-1.
ind = np.where(sfre > 0)
ax.plot(redshifts[ind], np.log10(sfre[ind]), 'r', label='Shark')
common.prepare_legend(ax, ['r'])
#panel 2
ax = fig.add_subplot(312)
ytit = "$\\rm log_{10}(SFE_{\\rm H_2}/Gyr^{-1})$"
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
#note that only h^2 is needed because the volume provides h^3, and the SFR h^-1.
ind = np.where(sfreH2 > 0)
ax.plot(redshifts[ind], np.log10(sfreH2[ind]), 'r', label='Shark')
common.prepare_legend(ax, ['r'])
#panel 3
ax = fig.add_subplot(313)
ytit = "$\\rm log_{10}(M_{\\rm mol}/M_{\\rm atom})$"
common.prepare_ax(ax,
xmin,
xmax,
-3,
2,
xtit,
ytit,
locators=(0.1, 1, 0.1))
#note that only h^2 is needed because the volume provides h^3, and the SFR h^-1.
ind = np.where(mhrat > 0)
ax.plot(redshifts[ind], np.log10(mhrat[ind]), 'r', label='Shark')
common.prepare_legend(ax, ['r'])
common.savefig(outdir, fig, "cosmic_sfe.pdf")
def plot_baryon_fractions(plt, outdir, redshifts, mstar_dm, mcold_dm, mhot_dm,
meje_dm, mbar_dm):
fig = plt.figure(figsize=(9.5, 9.5))
xtit = "$\\rm redshift$"
ytit = "$\\rm log_{10}(\\rho/\\rho_{\\rm m})$"
ax = fig.add_subplot(111)
common.prepare_ax(ax, 0, 10, -4, 0.5, xtit, ytit, locators=(0.1, 1, 0.1))
ax.plot(redshifts, mstar_dm, 'k', label='stars')
ax.plot(redshifts, mcold_dm, 'b', label='ISM gas')
ax.plot(redshifts, mhot_dm, 'r', label='halo gas')
ax.plot(redshifts, meje_dm, 'g', label='ejected gas')
ax.plot(redshifts, mbar_dm, 'm', label='total baryons')
yplot = [0.1866920152, 0.1866920152]
xplot = [0, 10]
ax.plot(xplot,
np.log10(yplot),
'k',
linestyle='dashed',
label='Universal $f_{\\rm baryon}$')
common.prepare_legend(ax, ['k', 'b', 'r', 'g', 'm', 'k'])
common.savefig(outdir, fig, "baryon_frac.pdf")
def plot_caustic_halos(plt, outdir, sats_vproj, sats_rproj, sats_type):
fig = plt.figure(figsize=(9,9))
xtit = "$r/r_{\\rm vir}$"
ytit = "$v_{\\rm r}/v_{\\rm vir}$"
xmin, xmax, ymin, ymax = 0, 1.2, -5, 5
xleg = xmin + 0.02 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
ax = fig.add_subplot(111)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
#ind = np.where(sats_type == 2)
#xdata = sats_rproj[ind]
#ydata = sats_vproj[ind]
#us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs)
ind = np.where(sats_type == 2)
xdata = sats_rproj[ind]
ydata = sats_vproj[ind]
ax.plot(xdata,ydata,'ro',markersize=0.7) #, **contour_kwargs)
ind = np.where(sats_type == 1)
xdata = sats_rproj[ind]
ydata = sats_vproj[ind]
ax.plot(xdata,ydata,'ko',markersize=0.7) #, **contour_kwargs)
common.savefig(outdir, fig, 'caustic_groups.pdf')
def plot_curves(file_name, type_name, show_ylabel=True, show_legend=True):
json_file = os.path.join(common.result_folder, file_name)
xlabel = 'num_dexels'
ylabel = 'time'
with open(json_file, 'r') as f:
database = json.load(f)
# Get list of x entries
x_array = set()
for entries in database.values():
x_array = x_array | set(entry[xlabel] for entry in entries)
x_array = sorted(x_array)
# Get list of y entries
y_arrays = {}
for i, entries in enumerate(database.values()):
for entry in entries:
key = (entry['operation'], entry['radius_relative'])
if key not in y_arrays:
y_arrays[key] = numpy.zeros(
(len(database.keys()), len(x_array)))
j = x_array.index(entry[xlabel])
y_arrays[key][i, j] = entry[ylabel] / 1000
# Draw plot
colors = itertools.cycle(('C0', 'C1', 'C2', 'C3'))
markers = itertools.cycle(('o', '*', 'd', '^')) # ',', +', '.', 'o', '*')
linestyles = itertools.cycle(('-', '--', '-.', ':'))
# plt.figure()
fig, ax = common.newfig(1)
show_conv = (type_name == 'loglog')
for key, y_array in sorted(y_arrays.items()):
r = key[1]
c = next(colors)
e = key[0].title()
name = '{}, $r = {}$'.format(e, r)
common.region_plot(x_array, y_array, c, c, name, next(linestyles),
next(markers), show_conv)
if type_name == 'loglog':
plt.loglog()
ax.xaxis.set_major_formatter(plticker.LogFormatter(labelOnlyBase=True))
ax.xaxis.set_minor_formatter(
plticker.LogFormatter(labelOnlyBase=False,
minor_thresholds=(4, 0.5)))
plt.xlabel(common.legends[xlabel], fontsize='large')
if show_ylabel:
plt.ylabel(common.legends[ylabel], fontsize='large')
# if show_legend:
plt.legend(loc='upper left')
result_plot = os.path.join(
common.result_folder, ylabel + '_' + os.path.splitext(file_name)[0] +
'_' + type_name + '.pdf')
# plt.savefig(result_plot)
common.savefig(result_plot)
def myplot(tag3, ylabel):
data = df[(df.tag1 != 'query') & (df.tag1 != 'ks') & (df.tag3 == tag3)]
data = data.sort_values('var')
for chain, group in data.groupby('tag1'):
fig, ax = plt.subplots()
for single, grp in group.groupby('tag2'):
grp.plot(ax=ax, kind='line', x='var', y='value', label=single)
ax.set_ylim((0, ax.yaxis.get_data_interval()[1]))
ax.margins(y=0.1)
ax.set_ylabel(ylabel)
ax.set_xlabel(' ')
cmn.savefig('top_' + chain + '_' + tag3, fig)
def plot_h1h2_gas_fraction(plt, output_dir, mhr_relation, mhr_relation_cen,
mhr_relation_sat):
fig = plt.figure(figsize=(5, 4.5))
ax = fig.add_subplot(111)
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10}(M_{\\rm H_2}/M_{\\rm HI})$"
prepare_ax(ax, 8, 12, -3, 1.0, xtit, ytit)
# Predicted SMHM
ind = np.where(mhr_relation[0, :] != 0)
xplot = xmf[ind]
yplot = mhr_relation[0, ind]
errdn = mhr_relation[1, ind]
errup = mhr_relation[2, ind]
ax.errorbar(xplot, yplot[0], color='k', label="all galaxies")
ax.errorbar(xplot,
yplot[0],
yerr=[errdn[0], errup[0]],
ls='None',
mfc='None',
ecolor='k',
mec='k',
marker='+',
markersize=2)
ind = np.where(mhr_relation_cen[0, :] != 0)
xplot = xmf[ind]
yplot = mhr_relation_cen[0, ind]
ax.errorbar(xplot,
yplot[0],
color='b',
linestyle='dotted',
label="centrals")
ind = np.where(mhr_relation_sat[0, :] != 0)
xplot = xmf[ind]
yplot = mhr_relation_sat[0, ind]
ax.errorbar(xplot,
yplot[0],
color='r',
linestyle='dashed',
label="satelites")
common.prepare_legend(ax, ['k', 'b', 'r', 'grey', 'grey'])
common.savefig(output_dir, fig, "HIH2_gas_fraction.pdf")
def conduct(x, data, noise, filename):
"""Conduct one experiment with data."""
# Analyze the behavior of the non-normalized residuals first.
lscf_def_noise = LSConservativeFormulation(x, data)
lscf_def_noise.fit()
fig, ax = plt.subplots(1, 2, figsize=FIG_SIZE)
ax[0].bar(x, lscf_def_noise.residuals)
ax[0].set_xlabel(r'$x$')
ax[0].set_ylabel('Non-normalized residuals')
kde = stats.gaussian_kde(lscf_def_noise.residuals)
__, bins, __ = ax[1].hist(lscf_def_noise.residuals, density=True)
xx = np.linspace(bins.min(), bins.max(), num=101)
ax[1].plot(xx, kde(xx))
ax[1].set_xlabel(r'$R$')
ax[1].set_ylabel(r'Residuals PDF')
fig.tight_layout(pad=0.1)
savefig(fig, 'residuals-' + filename)
# Now fit and plot the fitting results.
ols = OLS(x, data, noise)
ols.fit()
lscf = LSConservativeFormulation(x, data, noise)
lscf.fit()
lsbag = BadAndGoodLS(x, data, noise=noise, beta=6/len(x), gamma=50)
lsbag.fit()
ols.print_result()
lscf.print_result()
lsbag.print_result()
fig, ax = plt.subplots(1, 1, figsize=FIG_SIZE)
ax.plot(x, data, 'o', label='Data')
ax.plot(x, ols.result, '-', label='LS, ordinary')
ax.plot(x, lscf.result, '--', label='LS, extension 1')
ax.plot(x, lsbag.result, '-.', label='LS, extension 2')
ax.legend(loc='best')
ax.set_xlabel(r'$x$')
ax.set_ylabel('Observations and fit')
fig.tight_layout(pad=0.1)
savefig(fig, filename)
def plot_mass_cosmic_density(plt, outdir, redshifts, mcold, mHI, mH2):
fig = plt.figure(figsize=(5, 4.5))
ax = fig.add_subplot(111)
plt.subplots_adjust(bottom=0.15, left=0.15)
xtit = "$\\rm Lookback\,time/Gyr$"
ytit = "$\\rm log_{10}(\\Omega_{\\rm gas})$"
common.prepare_ax(ax,
0,
13.5,
-4,
-2.7,
xtit,
ytit,
locators=(0.1, 1, 0.1, 1))
ax2 = ax.twiny()
ax2.set_xlim(ax.get_xlim())
new_tick_locations = np.array([0., 2., 4., 6., 8., 10., 12.])
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(us.redshift(new_tick_locations), fontsize=12)
ax2.set_xlabel("redshift", fontsize=13)
#note that only h^2 is needed because the volume provides h^3, and the SFR h^-1.
ax.plot(us.look_back_time(redshifts),
mcold + np.log10(Omegab) - np.log10(XH),
'k',
label='total neutral ISM')
ax.plot(us.look_back_time(redshifts),
mHI + np.log10(Omegab) - np.log10(XH),
'b',
linestyle='dotted',
label='atomic')
ax.plot(us.look_back_time(redshifts),
mH2 + np.log10(Omegab) - np.log10(XH),
'r',
linestyle='dashed',
label='molecular')
common.prepare_legend(ax, ['k', 'b', 'r'], loc=1)
common.savefig(outdir, fig, "omega_neutral.pdf")
def plot_sizes_combined(plt, outdir, rcomb):
fig = plt.figure(figsize=(5, 4.5))
# Total ##################################
xtit = "$\\rm log_{10} (\\rm M_{\\rm stars, total}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm r_{\\rm 50, comb}/kpc)$"
xmin, xmax, ymin, ymax = 8, 12, -0.5, 2
ax = fig.add_subplot(111)
plt.subplots_adjust(bottom=0.15, left=0.15)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
#Predicted size-mass for disks
ind = np.where(rcomb[0, 0, :] != 0)
xplot = xmf[ind]
yplot = rcomb[0, 0, ind]
errdn = rcomb[0, 1, ind]
errup = rcomb[0, 2, ind]
ax.errorbar(xplot,
yplot[0],
yerr=[errdn[0], errup[0]],
ls='None',
mfc='None',
ecolor='k',
mec='k',
marker='o',
label="Shark disk+bulge combined")
common.prepare_legend(ax, ['k', 'k', 'k'], loc=2)
common.savefig(outdir, fig, 'sizes_combined.pdf')
def plot_scaling_z(plt, outdir, obsdir, snap, SFRMstar, R50Mstar, R50Mstar30,
MBHMstar, SigmaMstar30, ZstarMstar, ZSFMstar, AgeSMstar,
SFRMstar30, R50pMstar30):
#define observaiton of the MS at z=0 to be plotted
def obs_mainseq_z0():
#best fit from Davies et al. (2016)
xdataD16 = [9.3, 10.6]
ydataD16 = [-0.39, 0.477]
ax.plot(xdataD16,
ydataD16,
color='b',
linestyle='dashdot',
linewidth=4,
label='Davies+16')
#SDSS z=0 relation
lm, SFR = common.load_observation(obsdir, 'SFR/Brinchmann04.dat',
(0, 1))
hobs = 0.7
#add cosmology correction plus IMF correction that goes into the stellar mass.
corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09
# apply correction to both stellar mass and SFRs.
ax.plot(lm[0:35] + corr_cos,
SFR[0:35] + corr_cos,
color='PaleVioletRed',
linewidth=3,
linestyle='dashed',
label='Brinchmann+04')
ax.plot(lm[36:70] + corr_cos,
SFR[36:70] + corr_cos,
color='PaleVioletRed',
linewidth=5,
linestyle='dotted')
ax.plot(lm[71:len(SFR)] + corr_cos,
SFR[71:len(SFR)] + corr_cos,
color='PaleVioletRed',
linewidth=5,
linestyle='dotted')
########################### will plot main sequence for all stellar particles in the subhalo
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm SFR/M_{\odot} yr^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, -5, 1.5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#observations z=0
if (z == 0):
obs_mainseq_z0()
#VR
ind = np.where(SFRMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SFRMstar[idx, 0, ind]
errdn = SFRMstar[idx, 1, ind]
errup = SFRMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'PaleVioletRed', 'k'])
common.savefig(outdir, fig, "main_sequence_z_comp_obs.pdf")
########################### will plot main sequence for 30kpc aperture
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm SFR_{\\rm 30kpc}/M_{\odot} yr^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, -5, 1.5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#observations z=0
if (z == 0):
obs_mainseq_z0()
#VR
ind = np.where(SFRMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SFRMstar30[idx, 0, ind]
errdn = SFRMstar30[idx, 1, ind]
errup = SFRMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'PaleVioletRed', 'k'])
common.savefig(outdir, fig, "main_sequence_30kpc_z_comp_obs.pdf")
########################### will plot r50 vs stellar mass for all stellar particles in the subhalo
#define observations first
def plot_gama_size_mass():
m, r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat',
[0, 1])
ax.plot(m[0:36],
r[0:36],
linestyle='dotted',
color='b',
label='L16 disks')
ax.plot(m[38:83], r[38:83], linestyle='dotted', color='b')
ax.plot(m[85:128], r[85:129], linestyle='dotted', color='b')
m, r = common.load_observation(obsdir, 'SizesAndAM/rbulge_L16.dat',
[0, 1])
ax.plot(m[0:39],
r[0:39],
linestyle='dotted',
color='darkgreen',
label='L16 bulges')
ax.plot(m[41:76], r[41:76], linestyle='dotted', color='darkgreen')
ax.plot(m[78:115], r[78:115], linestyle='dotted', color='darkgreen')
xtit = "$\\rm log_{10} (\\rm M_{\\star,tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,tot}/pkpc)$"
xmin, xmax, ymin, ymax = 7, 12, -0.3, 2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
if (z == 0):
plot_gama_size_mass()
#VR
ind = np.where(R50Mstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50Mstar[idx, 0, ind] + 3.0
errdn = R50Mstar[idx, 1, ind]
errup = R50Mstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'darkgreen', 'k'],
loc='upper left')
common.savefig(outdir, fig, "r50_Mstar_z_comp_obs.pdf")
################## will plot r50 vs stellar mass for quantities measured within 30kpc
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc}/pkpc)$"
xmin, xmax, ymin, ymax = 7, 12, -0.3, 2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
if (z == 0):
plot_gama_size_mass()
#VR
ind = np.where(R50Mstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50Mstar30[idx, 0, ind] + 3.0
errdn = R50Mstar30[idx, 1, ind]
errup = R50Mstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'darkgreen', 'k'],
loc='upper left')
common.savefig(outdir, fig, "r50_Mstar_30kpc_z_comp_obs.pdf")
################## will plot r50 vs stellar mass for quantities measured within 30kpc, but in this case the r50 is projected
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc,2D}/pkpc)$"
xmin, xmax, ymin, ymax = 7, 12, -0.3, 2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
if (z == 0):
plot_gama_size_mass()
#VR
ind = np.where(R50pMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50pMstar30[idx, 0, ind] + 3.0
errdn = R50pMstar30[idx, 1, ind]
errup = R50pMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'darkgreen', 'k'],
loc='upper left')
common.savefig(outdir, fig, "r50_projected_Mstar_30kpc_z_comp_obs.pdf")
########################### will plot stellar velocity dispersion vs. stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\sigma_{\\star,30kpc}/km s^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, 1, 3
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#VR
ind = np.where(SigmaMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SigmaMstar30[idx, 0, ind]
errdn = SigmaMstar30[idx, 1, ind]
errup = SigmaMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
#observations
if (z == 0):
lm, sig, sigdn, sigup = common.load_observation(
obsdir, 'StellarPops/vdS19-sigma.csv', [0, 1, 2, 3])
sig = np.log10(sig)
sigdn = np.log10(sigdn)
sigup = np.log10(sigup)
common.errorbars(ax,
lm,
sig,
sigdn,
sigup,
'b',
'D',
label='van de Sande+19')
if idx == 0:
common.prepare_legend(ax, ['k'], loc='upper left')
common.savefig(outdir, fig, "vdisp_Mstar_30kpc_z_comp_obs.pdf")
############ will plot stellar metallicity-stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm Z_{\star}/Z_{\\odot})$"
xmin, xmax, ymin, ymax = 7, 12, -2, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
plt.subplots_adjust(left=0.2)
#VR
ind = np.where(ZstarMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = ZstarMstar[idx, 0, ind] - log10zsun
errdn = ZstarMstar[idx, 1, ind]
errup = ZstarMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
#observations
if (z == 0):
lm, mz, mzdn, mzup = common.load_observation(
obsdir, 'MZR/MSZR-Gallazzi05.dat', [0, 1, 2, 3])
common.errorbars(ax,
lm[0:7],
mz[0:7],
mzdn[0:7],
mzup[0:7],
'b',
'D',
label='Kirby+13')
common.errorbars(ax,
lm[7:22],
mz[7:22],
mzdn[7:22],
mzup[7:22],
'b',
'o',
label='Gallazzi+05')
if idx == 0:
common.prepare_legend(ax, ['k', 'b', 'b'], loc='lower right')
common.savefig(outdir, fig, "zstar_mstar_30kpc_z_comp_obs.pdf")
################ will plot star-forming gas metallicity vs. stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm Z_{\\rm SF,gas}/Z_{\\odot})$"
xmin, xmax, ymin, ymax = 7, 12, -2, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
plt.subplots_adjust(left=0.2)
if (z == 0):
#MZR z=0
corrzsun = 8.69 #solar oxygen abundance in units of 12 + log(O/H)
hobs = 0.72
#add cosmology correction plus IMF correction that goes into the stellar mass.
corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09
lm, mz, mzdn, mzup = common.load_observation(
obsdir, 'MZR/MMAdrews13.dat', [0, 1, 2, 3])
hobs = 0.7
#add cosmology correction plus IMF correction that goes into the stellar mass.
corr_cos = np.log10(pow(hobs, 2) / pow(h0, 2)) - 0.09
common.errorbars(ax,
lm + corr_cos,
mz - corrzsun,
mzdn - corrzsun,
mzup - corrzsun,
'b',
's',
label='Andrews+13')
#correction for Tremonti is the same.
lm, mz, mzdn, mzup = common.load_observation(
obsdir, 'MZR/Tremonti04.dat', [0, 1, 2, 3])
common.errorbars(ax,
lm + corr_cos,
mz - corrzsun,
mzdn - corrzsun,
mzup - corrzsun,
'b',
'o',
label="Tremonti+04")
#VR
ind = np.where(ZSFMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = ZSFMstar[idx, 0, ind] - log10zsun
errdn = ZSFMstar[idx, 1, ind]
errup = ZSFMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['k', 'b', 'b'], loc='lower right')
common.savefig(outdir, fig, "zsfgas_mstar_30kpc_z_comp_obs.pdf")
################ will plot stellar ages vs stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm age_{\\star}/Gyr)$"
xmin, xmax, ymin, ymax = 7, 12, 0, 1.1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
plt.subplots_adjust(left=0.2)
#VR
ind = np.where(AgeSMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = AgeSMstar[idx, 0, ind]
errdn = AgeSMstar[idx, 1, ind]
errup = AgeSMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
#observations
if (z == 0):
lm, age, agedn, ageup = common.load_observation(
obsdir, 'StellarPops/vdS19-age.csv', [0, 1, 2, 3])
common.errorbars(ax,
lm,
age,
agedn,
ageup,
'b',
'D',
label='van de Sande+19')
if idx == 0:
common.prepare_legend(ax, ['k'])
common.savefig(outdir, fig, "starage_mstar_z_comp_obs.pdf")
################ will plot black hole mass vs stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm M_{\\rm BH}/M_{\odot})$"
xmin, xmax, ymin, ymax = 7, 12, 6, 11
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
plt.subplots_adjust(left=0.2)
if (z == 0):
#BH-bulge relation
mBH_M13, errup_M13, errdn_M13, mBH_power, mbulge_M13 = common.load_observation(
obsdir, 'BHs/MBH_sigma_Mbulge_McConnelMa2013.dat',
[0, 1, 2, 3, 7])
ind = np.where((mBH_M13 > 0) & (mbulge_M13 > 0))
xobs = np.log10(mbulge_M13[ind])
yobs = np.log10(mBH_M13[ind] * pow(10.0, mBH_power[ind]))
lerr = np.log10(
(mBH_M13[ind] - errdn_M13[ind]) * pow(10.0, mBH_power[ind]))
herr = np.log10(
(mBH_M13[ind] + errup_M13[ind]) * pow(10.0, mBH_power[ind]))
ax.errorbar(xobs,
yobs,
yerr=[yobs - lerr, herr - yobs],
ls='None',
mfc='None',
ecolor='r',
mec='r',
marker='^',
label="McConnell & Ma 2013")
#BH-bulge relation
mBH_H04, errup_H04, errdn_H04, mbulge_H04 = common.load_observation(
obsdir, 'BHs/MBH_sigma_Mbulge_HaeringRix2004.dat',
[0, 1, 2, 4])
xobs = np.log10(mbulge_H04)
yobs = xobs * 0. - 999.
indx = np.where(mBH_H04 > 0)
yobs[indx] = np.log10(mBH_H04[indx])
lerr = yobs * 0. - 999.
indx = np.where((mBH_H04 - errdn_H04) > 0)
lerr[indx] = np.log10(mBH_H04[indx] - errdn_H04[indx])
herr = yobs * 0. + 999.
indx = np.where((mBH_H04 + errup_H04) > 0)
herr[indx] = np.log10(mBH_H04[indx] + errup_H04[indx])
ax.errorbar(xobs,
yobs,
yerr=[yobs - lerr, herr - yobs],
ls='None',
mfc='None',
ecolor='maroon',
mec='maroon',
marker='s',
label="Haering+04")
#VR
ind = np.where(MBHMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = MBHMstar[idx, 0, ind]
errdn = MBHMstar[idx, 1, ind]
errup = MBHMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'k', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='k',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='k',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['k', 'r', 'maroon'], loc='upper left')
common.savefig(outdir, fig, "blackhole_stellarmass_z_comp_obs.pdf")
def plot_SMHM_z(plt, outdir, z, massgal):
fig = plt.figure(figsize=(9.7, 11.7))
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo, DM}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
xmin, xmax, ymin, ymax = 10.5, 15, 7, 13
xleg = xmin + 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
#Moster et al. (2013) abundance matching SMHM relation
M10 = 11.590
M11 = 1.195
N10 = 0.0351
N11 = -0.0247
beta10 = 1.376
beta11 = -0.826
gamma10 = 0.608
gamma11 = 0.329
subplots = (321, 322, 323, 324, 325, 326)
all_labels = (('Shark', 'Moster+13', 'Behroozi+13'), )
for i, (z, subplot) in enumerate(zip(z, subplots)):
labels = all_labels[0]
# z=0 ##################################
ax = fig.add_subplot(subplot)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.tick_params(labelsize=13)
ax.text(xleg, yleg, 'z=%s' % str(z))
#Predicted SMHM
ind = np.where(massgal[i, 0, :] != 0)
xplot = xmf[ind]
yplot = massgal[i, 0, ind]
errdn = massgal[i, 1, ind]
errup = massgal[i, 2, ind]
if not labels:
ax.errorbar(xplot, yplot[0], color='k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='grey',
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='grey',
interpolate=True)
else:
ax.errorbar(xplot, yplot[0], color='k', label=labels[0])
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='grey',
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='grey',
interpolate=True)
M1 = pow(10.0, M10 + M11 * z / (z + 1))
N = N10 + N11 * z / (z + 1)
beta = beta10 + beta11 * z / (z + 1)
gamma = gamma10 + gamma11 * z / (z + 1)
mh = pow(10.0, xmf)
m = mh * 2 * N * pow((pow(mh / M1, -beta) + pow(mh / M1, gamma)), -1)
if not labels:
ax.plot(xmf, np.log10(m), 'r', linestyle='dashed', linewidth=3)
else:
ax.plot(xmf,
np.log10(m),
'r',
linestyle='dashed',
linewidth=3,
label=labels[1])
a = 1.0 / (1.0 + z)
nu = np.exp(-4 * a * a)
log_epsilon = -1.777 + (-0.006 * (a - 1)) * nu
M1 = 11.514 + (-1.793 * (a - 1) - 0.251 * z) * nu
alpha = -1.412 + 0.731 * nu * (a - 1)
delta = 3.508 + (2.608 * (a - 1) - 0.043 * z) * nu
gamma = 0.316 + (1.319 * (a - 1) + 0.279 * z) * nu
Min = xmf - M1
fx = -np.log10(pow(10, alpha * Min) + 1.0) + delta * pow(
np.log10(1 + np.exp(Min)), gamma) / (1 + np.exp(pow(10, -Min)))
f = -0.3 + delta * pow(np.log10(2.0), gamma) / (1 + np.exp(1))
m = log_epsilon + M1 + fx - f
if not labels:
ax.plot(xmf, m, 'b', linestyle='dashdot', linewidth=3)
else:
ax.plot(xmf,
m,
'b',
linestyle='dashdot',
linewidth=3,
label=labels[2])
if labels:
common.prepare_legend(ax, ['r', 'b', 'k'], loc=4)
common.savefig(outdir, fig, 'SMHM_z.pdf')
def plot_BMHM_z(plt, outdir, massbar, massbar_inside):
fig = plt.figure(figsize=(5, 6))
xtit = ""
ytit = ""
xmin, xmax, ymin, ymax = 10, 15, -1, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymin + 0.15 * (ymax - ymin)
ax = fig.add_subplot(311)
plt.subplots_adjust(left=0.17)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=0')
#Predicted SMHM
ind = np.where(massbar[0, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar[0, 0, ind]
errdn = massbar[0, 1, ind]
errup = massbar[0, 2, ind]
ax.errorbar(xplot, yplot[0], color='k', label="all baryons")
ax.errorbar(xplot,
yplot[0],
yerr=[errdn[0], errup[0]],
ls='None',
mfc='None',
ecolor='k',
mec='k',
marker='+',
markersize=2)
ind = np.where(massbar_inside[0, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar_inside[0, 0, ind]
errdn = massbar_inside[0, 1, ind]
errup = massbar_inside[0, 2, ind]
ax.plot(xplot,
yplot[0],
color='b',
linestyle='dotted',
label="inside halos")
xline = [10.0, 15.0]
yline = [0.0, 0.0]
ax.plot(xline, yline, 'r', linestyle='dashed')
common.prepare_legend(ax, ['b', 'k'], loc=2)
# z=0.5 ##################################
#ax = fig.add_subplot(222)
#common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1))
#ax.text(xleg,yleg, 'z=0.5')
#Predicted SMHM
#ind = np.where(massbar[1,0,:] != 0)
#xplot = xmf[ind]
#yplot = massbar[1,0,ind]
#errdn = massbar[1,1,ind]
#errup = massbar[1,2,ind]
#ax.errorbar(xplot,yplot[0],color='k', label="Shark")
#ax.errorbar(xplot,yplot[0],yerr=[errdn[0],errup[0]], ls='None', mfc='None', ecolor = 'k', mec='k',marker='+',markersize=2)
#ax.plot(xmf,xmf,'r', linestyle='dashed')
# z=1 ##################################
ax = fig.add_subplot(312)
xtit = ""
ytit = "$\\rm log_{10} (\\rm M_{\\rm bar}(\\Omega_{\\rm DM}/\\Omega_{\\rm b})/\\rm M_{\\rm halo, DM})$"
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=1')
# Predicted SMHM
ind = np.where(massbar[2, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar[2, 0, ind]
errdn = massbar[2, 1, ind]
errup = massbar[2, 2, ind]
ax.errorbar(xplot, yplot[0], color='k', label="Shark")
ax.errorbar(xplot,
yplot[0],
yerr=[errdn[0], errup[0]],
ls='None',
mfc='None',
ecolor='k',
mec='k',
marker='+',
markersize=2)
ind = np.where(massbar_inside[2, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar_inside[2, 0, ind]
errdn = massbar_inside[2, 1, ind]
errup = massbar_inside[2, 2, ind]
ax.plot(xplot, yplot[0], color='b', linestyle='dotted')
ax.plot(xline, yline, 'r', linestyle='dashed')
# z=1 ##################################
ax = fig.add_subplot(313)
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo, DM}/M_{\odot})$"
ytit = ""
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=2')
#Predicted SMHM
ind = np.where(massbar[3, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar[3, 0, ind]
errdn = massbar[3, 1, ind]
errup = massbar[3, 2, ind]
ax.errorbar(xplot, yplot[0], color='k', label="Shark")
ax.errorbar(xplot,
yplot[0],
yerr=[errdn[0], errup[0]],
ls='None',
mfc='None',
ecolor='k',
mec='k',
marker='+',
markersize=2)
ind = np.where(massbar_inside[3, 0, :] != 0)
xplot = xmf[ind]
yplot = massbar_inside[3, 0, ind]
errdn = massbar_inside[3, 1, ind]
errup = massbar_inside[3, 2, ind]
ax.plot(xplot, yplot[0], color='b', linestyle='dotted')
ax.plot(xline, yline, 'r', linestyle='dashed')
common.savefig(outdir, fig, 'BMHM_z.pdf')
def plot_con_lambda(plt, outdir, con, lam, snap):
xtit="$\\rm log_{10} (\\rm M_{\\rm tot}/M_{\odot})$"
ytit="$\\rm concentration$"
xmin, xmax, ymin, ymax = 8, 14, 0.5, 30
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#Predicted HMF
ind = np.where(con[0,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = con[0,idx,0,ind]
errdn = con[0,idx,1,ind]
errup = con[0,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', label ='VR hosts')
if idx > 0:
ax.plot(xplot,yplot[0],'r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True)
ind = np.where(con[1,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = con[1,idx,0,ind]
errdn = con[1,idx,1,ind]
errup = con[1,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh')
if idx > 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['r','r'], loc='upper left')
common.savefig(outdir, fig, "con_mtot_z.pdf")
xtit="$\\rm log_{10} (\\rm M_{\\rm tot}/M_{\odot})$"
ytit="$\lambda_{\\rm Bullock}$"
xmin, xmax, ymin, ymax = 8, 14, 0.01, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.3 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
plt.subplots_adjust(left=0.15)
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
ax.set_yscale("log")
#Predicted HMF
ind = np.where(lam[0,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = lam[0,idx,0,ind]
errdn = lam[0,idx,1,ind]
errup = lam[0,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', label ='VR hosts')
if idx > 0:
ax.plot(xplot,yplot[0],'r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True)
ind = np.where(lam[1,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = lam[1,idx,0,ind]
errdn = lam[1,idx,1,ind]
errup = lam[1,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh')
if idx > 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['r','r'], loc='upper center')
common.savefig(outdir, fig, "lambda_mtot_z.pdf")
def plot_halomf_z(plt, outdir, obs_dir, snap, vol_eagle, hist, histsh):
xtit="$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit="$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 8, 14, -6, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, subgn, mfofs, m200s, m200sm = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 3, 4, 6])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#HMF calc HMF calculated by Sheth & Tormen (2001)
lmp, dp = common.load_observation(obs_dir, 'hmf_calc_z%01d.dat'% z, [0, 7])
lmp_plot = np.log10(lmp) - np.log10(h0)
dp_plot = np.log10(dp) + np.log10(pow(h0,3.))
if(idx == 0):
ax.plot(lmp_plot,dp_plot,'b', label = 'HMF SMT01')
if(idx > 0):
ax.plot(lmp_plot,dp_plot,'b')
#HMF from SUBFIND
ind = np.where((mfofs > 0) & (subgn == 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(mfofs[ind]),bins=np.append(mbins,mupp))
histsfof = H
ind = np.where((m200s > 0) & (subgn == 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(m200s[ind]),bins=np.append(mbins,mupp))
hists200 = H
ind = np.where((m200sm > 0) & (subgn == 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(m200sm[ind]),bins=np.append(mbins,mupp))
hists200m = H
y = histsfof[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', label ='EAGLE L25 SF $M_{\\rm FOF}$')
if idx > 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g')
y = hists200[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed', label ='SF $M_{200\,crit}$')
if idx > 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed')
y = hists200m[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dotted', label ='SF $M_{200\,mean}$')
if idx > 0:
ax.plot(xmf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dotted')
#Predicted HMF
y = hist[0,idx,:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],y[ind],'r', linestyle='dashed', label ='VR $M_{200\,crit}$')
if idx > 0:
ax.plot(xmf[ind],y[ind],'r', linestyle='dashed')
y = histsh[idx,:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],y[ind],'r', label ='VR $M_{\\rm FOF}$')
if idx > 0:
ax.plot(xmf[ind],y[ind],'r')
y = hist[1,idx,:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],y[ind],'r', linestyle='dotted', label ='VR $M_{200\,mean}$')
if idx > 0:
ax.plot(xmf[ind],y[ind],'r', linestyle='dotted')
if idx == 0:
common.prepare_legend(ax, ['b','g','g','g','r','r','r'])
common.savefig(outdir, fig, "halomf_z.pdf")
def plot_r200_z(plt, outdir, r200, snap):
bin_it = functools.partial(us.wmedians, xbins=xmf)
xtit="$\\rm log_{10} (\\rm M_{\\rm 200crit}/M_{\odot})$"
ytit="$\\rm log_{10}(\\rm R_{\\rm 200crit}/pMpc)$"
xmin, xmax, ymin, ymax = 8, 14, -3, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, subgn, m200s, r200s = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 4, 5])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#HMF from SUBFIND
ind = np.where((m200s > 0) & (subgn == 0) & (sn == s) & (r200s > 0))
rplot = bin_it(x= np.log10(m200s[ind]), y = np.log10(r200s[ind])-3.0)
ind = np.where(rplot[0,:] != 0.)
xplot = xmf[ind]
yplot = rplot[0,ind]
errdn = rplot[1,ind]
errup = rplot[2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF')
if idx > 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
#Predicted HMF
ind = np.where(r200[0,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = r200[0,idx,0,ind]
errdn = r200[0,idx,1,ind]
errup = r200[0,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', label ='VR')
if idx > 0:
ax.plot(xplot,yplot[0],'r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b','r'])
common.savefig(outdir, fig, "r200M200_crit_z.pdf")
xtit="$\\rm log_{10} (\\rm M_{\\rm 200mean}/M_{\odot})$"
ytit="$\\rm log_{10}(\\rm R_{\\rm 200mean}/pMpc)$"
xmin, xmax, ymin, ymax = 8, 14, -3, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, subgn, m200s, r200s = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 6, 7])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#HMF from SUBFIND
ind = np.where((m200s > 0) & (subgn == 0) & (sn == s) & (r200s > 0))
rplot = bin_it(x= np.log10(m200s[ind]), y = np.log10(r200s[ind])-3.0)
ind = np.where(rplot[0,:] != 0.)
xplot = xmf[ind]
yplot = rplot[0,ind]
errdn = rplot[1,ind]
errup = rplot[2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF')
if idx > 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
#Predicted HMF
ind = np.where(r200[1,idx,0,:] != 0.)
xplot = xmf[ind]
yplot = r200[1,idx,0,ind]
errdn = r200[1,idx,1,ind]
errup = r200[1,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', label ='VR')
if idx > 0:
ax.plot(xplot,yplot[0],'r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b','r'])
common.savefig(outdir, fig, "r200M200_mean_z.pdf")
def plot_rivmax_vmax_z(plt, outdir, rvmax, hist, snap, vol_eagle):
bin_it = functools.partial(us.wmedians, xbins=xvf)
xtit="$\\rm log_{10} (\\rm V_{\\rm max}/km s^{-1})$"
ytit="$\\rm log_{10}(\\rm R_{\\rm V_{max}}/pMpc)$"
xmin, xmax, ymin, ymax = 1, 3.2, -3, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, subgn, rvs, vs = common.load_observation('/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/', 'SUBFIND-EAGLE-DATABASE.data', [2, 1, 18, 19])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#HMF from SUBFIND
ind = np.where((rvs > 0) & (subgn == 0) & (sn == s) & (vs> 0))
rplot = bin_it(x= np.log10(vs[ind]), y = np.log10(rvs[ind])-3.0)
ind = np.where((rvs > 0) & (subgn > 0) & (sn == s) & (vs> 0))
rplotsubh = bin_it(x= np.log10(vs[ind]), y = np.log10(rvs[ind])-3.0)
ind = np.where(rplot[0,:] != 0.)
xplot = xvf[ind]
yplot = rplot[0,ind]
errdn = rplot[1,ind]
errup = rplot[2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'b', linestyle='solid', label ='SF hosts')
if idx > 0:
ax.plot(xplot,yplot[0],'b', linestyle='solid')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
ind = np.where(rplotsubh[0,:] != 0.)
xplot = xvf[ind]
yplot = rplotsubh[0,ind]
errdn = rplotsubh[1,ind]
errup = rplotsubh[2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed', label ='SF subh')
if idx > 0:
ax.plot(xplot,yplot[0],'b', linestyle='dashed')
#Predicted HMF
ind = np.where(rvmax[0,idx,0,:] != 0.)
xplot = xvf[ind]
yplot = rvmax[0,idx,0,ind]
errdn = rvmax[0,idx,1,ind]
errup = rvmax[0,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', label ='VR hosts')
if idx > 0:
ax.plot(xplot,yplot[0],'r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.2,interpolate=True)
ind = np.where(rvmax[1,idx,0,:] != 0.)
xplot = xvf[ind]
yplot = rvmax[1,idx,0,ind]
errdn = rvmax[1,idx,1,ind]
errup = rvmax[1,idx,2,ind]
if idx == 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed', label ='VR subh')
if idx > 0:
ax.plot(xplot,yplot[0],'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b','b','r','r'], loc='upper left')
common.savefig(outdir, fig, "rvmax_vmax_z.pdf")
xtit="$\\rm log_{10} (\\rm V_{\\rm max}/km s^{-1})$"
ytit="$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 1, 3.2, -6, 1
xleg = xmax - 0.2 * (xmax-xmin)
yleg = ymax - 0.1 * (ymax-ymin)
fig = plt.figure(figsize=(5,10))
idx = [0,1,2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtitplot, ytit, locators=(0.1, 1, 0.1))
ax.text(xleg,yleg, 'z=%s' % (str(z)))
#HMF from SUBFIND
ind = np.where((rvs > 0) & (subgn == 0) & (sn == s) & (vs> 0))
H, bins_edges = np.histogram(np.log10(vs[ind]),bins=np.append(vbins,vupp))
histsvh = H
ind = np.where((rvs > 0) & (subgn > 0) & (sn == s) & (vs> 0))
H, bins_edges = np.histogram(np.log10(vs[ind]),bins=np.append(vbins,vupp))
histsvsubh = H
y = histsvh[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', label ='EAGLE L25 SF hosts')
if idx > 0:
ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g')
y = histsvsubh[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed', label ='SF subh')
if idx > 0:
ax.plot(xvf[ind],np.log10(y[ind]/vol_eagle/dm),'g', linestyle='dashed')
#Predicted HMF
y = hist[0,idx,:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xvf[ind],y[ind],'r', linestyle='solid', label ='VR hosts')
if idx > 0:
ax.plot(xvf[ind],y[ind],'r', linestyle='solid')
y = hist[1,idx,:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xvf[ind],y[ind],'r', linestyle='dashed', label ='VR subh')
if idx > 0:
ax.plot(xvf[ind],y[ind],'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['g','g','r','r'])
common.savefig(outdir, fig, "vmaxf_z.pdf")
def plot_lambda(plt, outdir, obsdir, lambdaH, lambda_jiang, lambda_mass, bt, ms, ssfr_z0):
lambda_allstar = lambda_jiang[3,:]
lambda_disk= lambda_jiang[0,:]
lambda_star= lambda_jiang[1,:]
lambda_gas = lambda_jiang[2,:]
fig = plt.figure(figsize=(5,12))
xtit = ""
ytit = "$\\rm log_{10}(\\lambda_{\\star})$"
xmin, xmax, ymin, ymax = -3, 0, -4, 0
xleg = xmin + 0.02 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
cutms = 7.5
med = np.zeros(shape = (3, len(xlf)))
ax = fig.add_subplot(411)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'all galaxies, stars+gas')
ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > cutms))
if(len(lambdaH[ind]) > 0):
#xdata = np.log10(lambdaH[ind])
#ydata = np.log10(lambda_allstar[ind])
#us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs)
coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_allstar[ind]))
ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3)))
med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True)
ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
xobs = xlf[ind]
yobs = med[0,ind]
yerrdn = med[1,ind]
yerrup = med[2,ind]
ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k')
#ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > 10))
#med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True)
#ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
#xobs = xlf[ind]
#yobs = med[0,ind]
#ax.plot(xobs, yobs[0], color='k',linestyle='dotted', label='$\\rm log_{10}(M_{\\star}/M_{\\odot}) > 10$')
#ind = np.where((lambdaH > 0) & (lambda_allstar > 0) & (lambda_allstar < 10) & (ms > 7.5) & (ms < 9))
#med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_allstar[ind]), xbins = xlf, low_numbers=True)
#ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
#xobs = xlf[ind]
#yobs = med[0,ind]
#ax.plot(xobs, yobs[0], color='k',linestyle='dashed', label='$\\rm 7.5<log_{10}(M_{\\star}/M_{\\odot})<9$')
ax = fig.add_subplot(412)
ytit = "$\\rm log_{10}(\\lambda_{\\rm disk})$"
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'disk-dominated, stars+gas')
ind = np.where((lambdaH > 0) & (lambda_disk > 0) & (bt < 0.5) & (ms > cutms))
#xdata = np.log10(lambdaH[ind])
#ydata = np.log10(lambda_disk[ind])
#us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs)
coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_disk[ind]))
ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3)))
med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_disk[ind]), xbins = xlf, low_numbers=True)
ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
xobs = xlf[ind]
yobs = med[0,ind]
yerrdn = med[1,ind]
yerrup = med[2,ind]
ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k')
ax = fig.add_subplot(413)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'disk-dominated, stars')
ind = np.where((lambdaH > 0) & (lambda_star > 0) & (bt < 0.5) & (ms > cutms))
xdata = np.log10(lambdaH[ind])
ydata = np.log10(lambda_star[ind])
us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs)
coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_star[ind]))
ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3)))
med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_star[ind]), xbins = xlf, low_numbers=True)
ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
xobs = xlf[ind]
yobs = med[0,ind]
yerrdn = med[1,ind]
yerrup = med[2,ind]
ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k')
ax = fig.add_subplot(414)
xtit = "$\\rm log_{10}(\\lambda_{\\rm halo})$"
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xmin + 0.02 * (xmax - xmin), yleg, 'disk-dominated, gas')
ind = np.where((lambdaH > 0) & (lambda_gas > 0) & (bt < 0.5) & (ms > cutms))
#xdata = np.log10(lambdaH[ind])
#ydata = np.log10(lambda_gas[ind])
#us.density_contour(ax, xdata, ydata, 30, 30) #, **contour_kwargs)
coeff = np.corrcoef(np.log10(lambdaH[ind]),np.log10(lambda_gas[ind]))
ax.text(xmin + 0.02 * (xmax - xmin), ymax - 0.25 * (ymax - ymin), 'R=%s' % str(np.around(coeff[0,1], decimals=3)))
med[:] = us.wmedians(x=np.log10(lambdaH[ind]), y=np.log10(lambda_gas[ind]), xbins = xlf, low_numbers=True)
ind = np.where((med[0,:] != 0) & (med[0,:] > -5))
xobs = xlf[ind]
yobs = med[0,ind]
yerrdn = med[1,ind]
yerrup = med[2,ind]
ax.errorbar(xobs, yobs[0], yerr=[yerrdn[0],yerrup[0]], ls='None', mfc='None', ecolor = 'grey', mec='grey',linestyle='solid', color='k')
common.savefig(outdir, fig, 'lambda_relation.pdf')
def plot_halomf_z(plt, outdir, obsdir, z, h0, hist, histsh, plotz):
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm halo}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 10.1, 15, -6, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(7, 7))
subplots = (221, 222, 223, 224)
idx = (0, 1, 2, 3)
for subplot, idx, z, plot_this_z in zip(subplots, idx, z, plotz):
ax = fig.add_subplot(subplot)
if (idx == 0 or idx == 2):
ytitplot = ytit
else:
ytitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytitplot,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#HMF calc HMF calculated by Sheth & Tormen (2001)
lmp, dp = common.load_observation(
obsdir,
'mf/HMF/mVector_PLANCK-SMT_z%s.dat' % str(z).replace('.', ''),
[0, 7])
lmp_plot = np.log10(lmp) - np.log10(h0)
dp_plot = np.log10(dp) + np.log10(pow(h0, 3.))
if idx == 0:
ax.plot(lmp_plot, dp_plot, 'b', label='HMF calc')
elif idx > 0:
ax.plot(lmp_plot, dp_plot, 'b')
#Predicted HMF
if plot_this_z:
y = hist[idx, :]
ind = np.where(y < 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', label='HMF Shark')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r')
y = histsh[idx, :]
ind = np.where(y < 0.)
if idx == 0:
ax.plot(xmf[ind],
y[ind],
'r',
linestyle='dashed',
label='SHMF Shark')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b', 'r', 'r'])
common.savefig(outdir, fig, "halomf_z.pdf")
xtit = "$\\rm log_{10}(\\rm M/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M}/{\\rm Mpc}^{-3})$"
xmin, xmax, ymin, ymax = 8, 15, -6, 1.2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 5))
idx = 0
ax = fig.add_subplot(111)
ytitplot = ytit
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytitplot,
locators=(0.1, 1, 0.1))
#lmp, dp = common.load_observation(obsdir, 'mf/HMF/mVector_PLANCK-SMT_z0_extended.dat', [0, 7])
#lmp_plot = np.log10(lmp) - np.log10(h0)
#dp_plot = np.log10(dp) + np.log10(pow(h0,3.))
#ax.plot(lmp_plot,dp_plot,'k')
#ax.plot(lmp_plot+np.log10(fb),dp_plot,'b', linestyle='dashed')
y = hist[idx, :]
ind = np.where((y < 0.) & (xmf > 10.3))
ax.plot(xmf[ind], y[ind], 'k')
ax.plot(xmf[ind] + np.log10(fb), y[ind], 'b', linestyle='dashed')
lm, p, dpdn, dpup = common.load_observation(obsdir,
'mf/SMF/GAMAII_BBD_GSMFs.dat',
[0, 1, 2, 3])
xobs = lm
indx = np.where(p > 0)
yobs = np.log10(p[indx])
ydn = yobs - np.log10(p[indx] - dpdn[indx])
yup = np.log10(p[indx] + dpup[indx]) - yobs
ax.errorbar(xobs[indx], yobs, ydn, yup, 'ro', label='Wright+17')
smfdensity = common.load_observation(
obsdir, 'Models/SharkVariations/SMF_FeedbackExperiment.dat', [0])
ynofeed = smfdensity[0:len(xsmf) - 1]
yreio = smfdensity[len(xsmf):2 * len(xsmf) - 1]
ystarf = smfdensity[2 * len(xsmf):3 * len(xsmf) - 1]
yfinal = smfdensity[3 * len(xsmf):4 * len(xsmf) - 1]
ind = np.where(ynofeed != 0)
ax.plot(xsmf[ind], ynofeed[ind], 'DarkRed')
ind = np.where(ystarf != 0)
ax.plot(xsmf[ind], ystarf[ind], 'LightCoral')
ind = np.where(yfinal != 0)
ax.plot(xsmf[ind], yfinal[ind], 'Orange')
common.prepare_legend(ax, ['r'])
common.savefig(outdir, fig, "halomf_z0.pdf")
def plot_specific_am(plt, outdir, obsdir, sam_stars_disk, sam_gas_disk_atom, sam_gas_disk_mol, sam_halo, sam_bar, sam_stars):
fig = plt.figure(figsize=(4.5,4.5))
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\rm disk}/kpc\\, km s^{-1})$"
xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 5
xleg = xmax - 0.5 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
# choose type of selection:
selec = 1 #disk-dominated galaxies
s = 0
# LTG ##################################
ax = fig.add_subplot(111)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=0 Shark-default', fontsize = 12)
ind = np.where(sam_halo[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_halo[s,0,ind,selec] + 3.0
errdn = sam_halo[s,1,ind,selec]
errup = sam_halo[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="DM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
#Predicted sAM-mass for disks in disk=dominated galaxies
ind = np.where(sam_stars[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_stars[s,0,ind,selec]+ 3.0
errdn = sam_stars[s,1,ind,selec]
errup = sam_stars[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='r',linestyle='dashed',label="stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.25,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.25,interpolate=True)
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_atom[s,1,ind,selec]
errup = sam_gas_disk_atom[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b',linestyle='dotted',label="atomic ISM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.25,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.25,interpolate=True)
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_mol[s,1,ind,selec]
errup = sam_gas_disk_mol[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g',linestyle='dashdot',label="molecular ISM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.25,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.25,interpolate=True)
common.prepare_legend(ax, ['k'], loc=2)
common.savefig(outdir, fig, 'specific_am_onlyz0.pdf')
fig = plt.figure(figsize=(9.5,9.5))
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\rm disk}/kpc\\, km s^{-1})$"
xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
subplots = (221, 222, 223, 224)
indz = (0, 1, 2, 3)
# choose type of selection:
selec = 1 #disk-dominated galaxies
# LTG ##################################
for z,s,p in zip(zlist, indz, subplots):
ax = fig.add_subplot(p)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=%s' % str(z))
ind = np.where(sam_halo[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_halo[s,0,ind,selec] + 3.0
errdn = sam_halo[s,1,ind,selec]
errup = sam_halo[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="DM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
#Predicted sAM-mass for disks in disk=dominated galaxies
ind = np.where(sam_stars[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_stars[s,0,ind,selec]+ 3.0
errdn = sam_stars[s,1,ind,selec]
errup = sam_stars[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='r',label="stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.5,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.5,interpolate=True)
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_atom[s,1,ind,selec]
errup = sam_gas_disk_atom[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b',label="atomic ISM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.5,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.5,interpolate=True)
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_mol[s,1,ind,selec]
errup = sam_gas_disk_mol[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g',label="molecular ISM")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.5,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.5,interpolate=True)
common.prepare_legend(ax, ['k'], loc=2)
common.savefig(outdir, fig, 'specific_am.pdf')
#plot angular momentum components separately.
fig = plt.figure(figsize=(15,5))
s = 0
#plot stars
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\star}/kpc\\, km s^{-1})$"
xmin, xmax, ymin, ymax = 8, 11.5, 1.5, 4
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
ax = fig.add_subplot(141)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
jst, jmole, jatomic, jbar = common.load_observation(obsdir, 'Models/SharkVariations/AngularMomentum.dat', [0,1,2,3])
jsL18 = np.zeros(shape = (3, len(xmf)))
jmolL18 = np.zeros(shape = (3, len(xmf)))
jatomL18 = np.zeros(shape = (3, len(xmf)))
jbarL18 = np.zeros(shape = (3, len(xmf)))
i = 0
p =0
for j in range(0,len(jst)/2):
jsL18[i,p] = jst[j]
jmolL18[i,p] = jmole[j]
jatomL18[i,p] = jatomic[j]
jbarL18[i,p] = jbar[j]
p = p + 1
if(p >= len(xmf)):
p = 0
i = i +1
#Predicted sAM-mass for disks in disk=dominated galaxies
ind = np.where(sam_stars[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_stars[s,0,ind,selec]+ 3.0
errdn = sam_stars[s,1,ind,selec]
errup = sam_stars[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='r')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.35,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.35,interpolate=True)
ind = np.where(sam_stars_disk[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_stars_disk[s,0,ind,selec]+ 3.0
errdn = sam_stars_disk[s,1,ind,selec]
errup = sam_stars_disk[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='r', linestyle='dotted')
ind = np.where(jsL18[0,:] != 0)
xplot = xmf[ind]
yplot = jsL18[0,ind]+ 3.0
errdn = jsL18[1,ind]
errup = jsL18[2,ind]
ax.plot(xplot,yplot[0],color='r',linestyle='dashed')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', linestyle='dashed', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', linestyle='dashed', alpha=0.2,interpolate=True)
#Read observational data.
ms, mserr, js, jserr = common.load_observation(obsdir, 'SizesAndAM/Posti18_AMdata.dat', [3,5,6,8])
errmdn = np.log10(ms) - np.log10(ms - mserr)
errmup = np.log10(ms + mserr) - np.log10(ms)
errjdn = np.log10(js) - np.log10(js - jserr)
errjup = np.log10(js + jserr) - np.log10(js)
ax.errorbar(np.log10(ms), np.log10(js), yerr=[errjdn,errjup], xerr=[errmdn,errmup],ls='None', mfc='None', ecolor = 'r', mec='r',marker='+',label="Posti+18")
bt, mbO14, msO14, mgO14, jbO14, jsO14, jgO14, jmolO14 = common.load_observation(obsdir, 'SizesAndAM/Obreschkow14_FP.dat', [2,6,7,8,11,12,14,15])
ax.plot(msO14, jsO14, 'ro',fillstyle='none', label="Obreschkow+14")
mgB17, msB17, errmsB17, mbB17, errmbB17, jgB17, errjgB17, jsB17, errjsB17, jbB17, errjbB17 = common.load_observation(obsdir, 'SizesAndAM/LITTLETHINGS_Butler16.dat', [1,3,4,5,6,7,8,9,10,11,12])
ax.errorbar(msB17, jsB17, yerr=[errjsB17,errjsB17], xerr=[errmsB17,errmsB17], ls='None', mfc='None', ecolor = 'r', mec='r',marker='s',label="Butler+16")
msC17, mgC17, mbC17, errupmbC17, errdnmbC17, JstarC17, JgasC17, errupJgasC17, errdnJgasC17, jbarC17, errupjbarC17, errdnjbarC17 = common.load_observation(obsdir, 'SizesAndAM/Chowdhury17.dat', [1,2,5,6,7,8,8,10,11,15,16,17])
ax.plot(msC17, JstarC17-msC17, 'rp', fillstyle='none',label="Chowdhury+17")
mbE17, jbE17 = common.load_observation(obsdir, 'SizesAndAM/Elson17.dat', [5,7])
mbE17 = np.log10(mbE17 * 1e8)
jbE17 = np.log10(jbE17)
#IDGalaxy Mstar errup errdn Mgas errup errdn Mbar errup errdn jstar errup errdn jgas errup errdn jbar errup errdn
(msK18, errupmsK18, errdnmsK18, mgK18, errupmgK18, errdnmgK18, mbK18, errupmbK18, errdnmbK18, jsK18, errupjsK18, errdnjsK18, jgK18,
errupjgK18, errdnjgK18, jbarK18, errupjbarK18, errdnjbarK18) = common.load_observation(obsdir, 'SizesAndAM/Kurapati18.dat', [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18])
ax.errorbar(msK18, jsK18, yerr=[errdnjsK18,errupjsK18], xerr=[errdnmsK18,errupmsK18],ls='None', mfc='None', ecolor = 'r', mec='r',marker='*',label="Kurapati+18")
common.prepare_legend(ax, ['r', 'r', 'r','r','r'], loc=2)
#plot molecular gas
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\rm H_2}/kpc\\, km s^{-1})$"
ax = fig.add_subplot(142)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_mol[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_mol[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_mol[s,1,ind,selec]
errup = sam_gas_disk_mol[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g', label="ISM/stars AM transfer")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.35,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.35,interpolate=True)
ind = np.where(jmolL18[0,:] != 0)
xplot = xmf[ind]
yplot = jmolL18[0,ind]+ 3.0
errdn = jmolL18[1,ind]
errup = jmolL18[2,ind]
ax.plot(xplot,yplot[0],color='g',linestyle='dashed', label="Lagos+18")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', linestyle='dashed', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', linestyle='dashed', alpha=0.2,interpolate=True)
ax.plot(msO14, jmolO14, 'go',fillstyle='none')
common.prepare_legend(ax, ['k'], loc=2)
#plot atomic gas
xtit = "$\\rm log_{10} (\\rm M_{\\star}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\rm HI}/kpc\\, km s^{-1})$"
ax = fig.add_subplot(143)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_gas_disk_atom[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_gas_disk_atom[s,0,ind,selec] + 3.0
errdn = sam_gas_disk_atom[s,1,ind,selec]
errup = sam_gas_disk_atom[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.35,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.35,interpolate=True)
ind = np.where(jatomL18[0,:] != 0)
xplot = xmf[ind]
yplot = jatomL18[0,ind]+ 3.0
errdn = jatomL18[1,ind]
errup = jatomL18[2,ind]
ax.plot(xplot,yplot[0],color='b',linestyle='dashed')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', linestyle='dashed', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', linestyle='dashed', alpha=0.2,interpolate=True)
ax.errorbar(msB17, jgB17, yerr=[errjgB17,errjgB17], xerr=[errmsB17,errmsB17,],ls='None', mfc='None', ecolor = 'b', mec='b',marker='s')
ax.plot(msO14, jgO14, 'bo',fillstyle='none')
ax.plot(msC17, JgasC17-mgC17, 'bp', fillstyle='none')
ax.errorbar(msK18, jgK18, yerr=[errdnjgK18,errupjgK18], xerr=[errdnmsK18,errupmsK18],ls='None', mfc='None', ecolor = 'b', mec='b',marker='*')
#plot total baryon
xtit = "$\\rm log_{10} (\\rm M_{\\rm bar}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\rm bar}/kpc\\, km s^{-1})$"
ax = fig.add_subplot(144)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(sam_bar[s,0,:,selec] != 0)
xplot = xmf[ind]
yplot = sam_bar[s,0,ind,selec] + 3.0
errdn = sam_bar[s,1,ind,selec]
errup = sam_bar[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.25,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.25,interpolate=True)
ind = np.where(jbarL18[0,:] != 0)
xplot = xmf[ind]
yplot = jbarL18[0,ind]+ 3.0
errdn = jbarL18[1,ind]
errup = jbarL18[2,ind]
ax.plot(xplot,yplot[0],color='k',linestyle='dashed')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', linestyle='dashed', alpha=0.1,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', linestyle='dashed', alpha=0.1,interpolate=True)
ax.errorbar(mbB17, jbB17, yerr=[errjbB17,errjbB17], xerr=[errmbB17,errmbB17],ls='None', mfc='None', ecolor = 'k', mec='k',marker='s')
ax.plot(mbO14, jbO14, 'ko',fillstyle='none')
ax.errorbar(mbC17, jbarC17, yerr=[errdnjbarC17,errupjbarC17], xerr=[errdnmbC17,errupmbC17], ls='None', mfc='None', ecolor = 'k', mec='k',marker='p')
ax.errorbar(mbK18, jbarK18, yerr=[errdnjbarK18,errupjbarK18], xerr=[errdnmbK18,errupmbK18],ls='None', mfc='None', ecolor = 'k', mec='k',marker='*')
ax.plot(mbE17, jbE17, 'k^', fillstyle='none', label='Elson17')
common.prepare_legend(ax, ['k'], loc=2)
common.savefig(outdir, fig, 'specific_am_z0_components.pdf')
for c in range (0,2):
print 'will change selection'
for i in range (0,3):
print 'will change within the same sample'
for x,y,z,a in zip(sam_stars_disk[s,i,:,c],sam_gas_disk_mol[s,i,:,c],sam_gas_disk_atom[s,i,:,c],sam_bar[s,i,:,c]):
print x,y,z,a
def 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):
fig = plt.figure(figsize=(9.5,9.5))
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm j_{\\star}/j_{\\rm halo}$)"
xmin, xmax, ymin, ymax = 10, 15, -3, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
subplots = (221, 222, 223, 224)
indz = (0, 1, 2, 3)
# choose type of selection:
selec = 1 #disk-dominated galaxies
# LTG ##################################
for z,s,p in zip(zlist, indz, subplots):
ax = fig.add_subplot(p)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=%s' % str(z))
ind = np.where(sam_ratio_halo_gal[s,0,:,selec] != 0)
xplot = xmfh[ind]
yplot = sam_ratio_halo_gal[s,0,ind,selec]
errdn = sam_ratio_halo_gal[s,1,ind,selec]
errup = sam_ratio_halo_gal[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="all stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
ind = np.where(sam_ratio_halo_disk[s,0,:,selec] != 0)
xplot = xmfh[ind]
yplot = sam_ratio_halo_disk[s,0,ind,selec]
errdn = sam_ratio_halo_disk[s,1,ind,selec]
errup = sam_ratio_halo_disk[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g',label="disk stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True)
ind = np.where(sam_ratio_halo_disk_gas[s,0,:,selec] != 0)
xplot = xmfh[ind]
yplot = sam_ratio_halo_disk_gas[s,0,ind,selec]
errdn = sam_ratio_halo_disk_gas[s,1,ind,selec]
errup = sam_ratio_halo_disk_gas[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b',label="disk gas")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
common.prepare_legend(ax, ['k'], loc=2)
common.savefig(outdir, fig, 'specific_am_ratio.pdf')
selec = 0 #disk-dominated galaxies
#plot specific AM vs. specific AM
fig = plt.figure(figsize=(9,8))
xtit = "$\\rm log_{10} (\\rm j_{\\rm halo}/kpc\,km\,s^{-1})$"
ytit = "$\\rm log_{10} (\\rm j_{\\star},j_{\\star,disk},j_{\\rm gas,disk}/kpc\,km\,s^{-1}$)"
xmin, xmax, ymin, ymax = 1,6,1,6
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
subplots = (221, 222, 223, 224)
indz = (0, 1, 2, 3)
zinplot = (0, 0.5, 1, 2)
# LTG ##################################
for z,s,p in zip(zinplot, indz, subplots):
ax = fig.add_subplot(p)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=%s' % str(z), fontsize=10)
#if(s == 0):
# ax.text(5.5,6.4,'Lagos+18',fontsize=14)
ind = np.where(sam_vs_sam_halo_gal[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_gal[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_gal[s,1,ind,selec]
errup = sam_vs_sam_halo_gal[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="all stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
ind = np.where(sam_vs_sam_halo_disk[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_disk[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_disk[s,1,ind,selec]
errup = sam_vs_sam_halo_disk[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g',label="disk stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True)
ind = np.where(sam_vs_sam_halo_disk_gas[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_disk_gas[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_disk_gas[s,1,ind,selec]
errup = sam_vs_sam_halo_disk_gas[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b',label="disk gas")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
xplot = [1,5]
ax.plot(xplot,xplot,color='grey',linestyle='dotted')
if(s == 0):
common.prepare_legend(ax, ['k','g','b'], loc=2)
common.savefig(outdir, fig, 'specific_am_halo_vs_galaxy.pdf')
selec = 0 #all galaxies
#plot specific AM vs. specific AM
fig = plt.figure(figsize=(5,5))
xtit = "$\\rm log_{10} (\\rm j_{\\rm halo}/kpc\,km\,s^{-1})$"
ytit = "$\\rm log_{10} (\\rm j_{\\star},j_{\\star,disk},j_{\\rm gas,disk}/kpc\,km\,s^{-1}$)"
xmin, xmax, ymin, ymax = 1,6,1,6
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
s = 0
# LTG ##################################
ax = fig.add_subplot(111)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=0', fontsize=10)
#if(s == 0):
# ax.text(5.5,6.4,'Lagos+18',fontsize=14)
ind = np.where(sam_vs_sam_halo_gal[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_gal[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_gal[s,1,ind,selec]
errup = sam_vs_sam_halo_gal[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="all stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
ind = np.where(sam_vs_sam_halo_disk[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_disk[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_disk[s,1,ind,selec]
errup = sam_vs_sam_halo_disk[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='g',label="disk stars")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.2,interpolate=True)
ind = np.where(sam_vs_sam_halo_disk_gas[s,0,:,selec] != 0)
xplot = xlf[ind]+3
yplot = sam_vs_sam_halo_disk_gas[s,0,ind,selec]+3
errdn = sam_vs_sam_halo_disk_gas[s,1,ind,selec]
errup = sam_vs_sam_halo_disk_gas[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='b',label="disk gas")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.2,interpolate=True)
xplot = [1,5]
ax.plot(xplot,xplot,color='grey',linestyle='dotted')
common.prepare_legend(ax, ['k','g','b'], loc=2)
common.savefig(outdir, fig, 'specific_am_halo_vs_galaxy_z0.pdf')
def plot_sizes(plt, outdir, obsdir, disk_size_cen, disk_size_sat, bulge_size, vmax_halo_gal):
print 'sizes disk centrals'
for i,j,p in zip(disk_size_cen[0,0,:],disk_size_cen[0,1,:],disk_size_cen[0,2,:]):
print i,j,p
print 'disk sizes satellites'
for i,j,p in zip(disk_size_sat[0,0,:],disk_size_sat[0,1,:],disk_size_sat[0,2,:]):
print i,j,p
print 'sizes bulges'
for i,j,p in zip(bulge_size[0,0,:],bulge_size[0,1,:],bulge_size[0,2,:]):
print i,j,p
rb, r16, r84 = common.load_observation(obsdir, 'Models/SharkVariations/SizeDisksAndBulges_OtherModels.dat', [0,1,2])
fig = plt.figure(figsize=(5,11.5))
xtit = "$\\rm log_{10} (\\rm M_{\\star,disk}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm r_{\\star,disk}/kpc)$"
xmin, xmax, ymin, ymax = 8, 12, -0.1, 2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
# LTG ##################################
ax = fig.add_subplot(311)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(8.1,1.7,'disks of centrals',fontsize=12)
#Predicted size-mass for disks in disk=dominated galaxies
ind = np.where(disk_size_cen[0,0,:] != 0)
xplot = xmf[ind]
yplot = disk_size_cen[0,0,ind]
errdn = disk_size_cen[0,1,ind]
errup = disk_size_cen[0,2,ind]
ax.plot(xplot,yplot[0],color='b',linestyle='solid',label="ISM/stars AM transfer")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='b', alpha=0.3,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='b', alpha=0.3,interpolate=True)
rdisk_am = rb[30:59]
rdisk_am16 = r16[30:59]
rdisk_am84 = r84[30:59]
ind = np.where(rdisk_am != 0)
xplot = xmf[ind]
yplot = rdisk_am[ind]
errdn = rdisk_am16[ind]
errup = rdisk_am84[ind]
ax.plot(xplot,yplot,color='b',linestyle='dashed', label="Lagos+18")
ax.fill_between(xplot,yplot,yplot-errdn, facecolor='b', linestyle='solid', alpha=0.4,interpolate=True)
ax.fill_between(xplot,yplot,yplot+errup, facecolor='b', linestyle='solid', alpha=0.4,interpolate=True)
#Lange et al. (2016)
m,r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat', [0,1])
ax.plot(m[0:36], r[0:36], linestyle='dotted',color='k')
ax.plot(m[38:83], r[38:83], linestyle='dotted',color='k')
ax.plot(m[85:128], r[85:129], linestyle='dotted',color='k')
common.prepare_legend(ax, ['b','b'], bbox_to_anchor=(0.005, 0.62))
ax = fig.add_subplot(312)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(8.1,1.7,'disks of satellites',fontsize=12)
#Predicted size-mass for disks in disk=dominated galaxies satellites
ind = np.where(disk_size_sat[0,0,:] != 0)
xplot = xmf[ind]
yplot = disk_size_sat[0,0,ind]
errdn = disk_size_sat[0,1,ind]
errup = disk_size_sat[0,2,ind]
ax.plot(xplot,yplot[0],color='g',linestyle='solid')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='g', alpha=0.3,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='g', alpha=0.3,interpolate=True)
rdisk_am = rb[0:29]
rdisk_am16 = r16[0:29]
rdisk_am84 = r84[0:29]
ind = np.where(rdisk_am != 0)
xplot = xmf[ind]
yplot = rdisk_am[ind]
errdn = rdisk_am16[ind]
errup = rdisk_am84[ind]
ax.plot(xplot,yplot,color='g',linestyle='dashed')
ax.fill_between(xplot,yplot,yplot-errdn, facecolor='g', linestyle='solid', alpha=0.4,interpolate=True)
ax.fill_between(xplot,yplot,yplot+errup, facecolor='g', linestyle='solid', alpha=0.4,interpolate=True)
#Lange et al. (2016)
m,r = common.load_observation(obsdir, 'SizesAndAM/rdisk_L16.dat', [0,1])
ax.plot(m[0:36], r[0:36], linestyle='dotted',color='k',label="L16 50th, 68th, 90th")
ax.plot(m[38:83], r[38:83], linestyle='dotted',color='k')
ax.plot(m[85:128], r[85:129], linestyle='dotted',color='k')
common.prepare_legend(ax, ['k'], bbox_to_anchor=(0.005, 0.67))
# ETGs ##################################
xtit = "$\\rm log_{10} (\\rm M_{\\star,bulge}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm r_{\\star,bulge}/kpc)$"
xmin, xmax, ymin, ymax = 8, 12, -0.35, 2
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
ax = fig.add_subplot(313)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(8.1,1.7,'bulges of all galaxies',fontsize=12)
#Predicted size-mass for bulges in bulge-dominated systems
ind = np.where(bulge_size[0,0,:] != 0)
if(len(xmf[ind]) > 0):
xplot = xmf[ind]
yplot = bulge_size[0,0,ind]
errdn = bulge_size[0,1,ind]
errup = bulge_size[0,2,ind]
ax.plot(xplot,yplot[0],color='r',linestyle='solid')
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='r', alpha=0.3,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='r', alpha=0.3,interpolate=True)
rdisk_am = rb[60:99]
rdisk_am16 = r16[60:99]
rdisk_am84 = r84[60:99]
ind = np.where(rdisk_am != 0)
xplot = xmf[ind]
yplot = rdisk_am[ind]
errdn = rdisk_am16[ind]
errup = rdisk_am84[ind]
ax.plot(xplot,yplot,color='r',linestyle='dashed')
ax.fill_between(xplot,yplot,yplot-errdn, facecolor='LightCoral', linestyle='solid', alpha=0.4,interpolate=True)
ax.fill_between(xplot,yplot,yplot+errup, facecolor='LightCoral', linestyle='solid', alpha=0.4,interpolate=True)
#Lange et al. (2016)
m,r = common.load_observation(obsdir, 'SizesAndAM/rbulge_L16.dat', [0,1])
ax.plot(m[0:39], r[0:39], linestyle='dotted',color='k')
ax.plot(m[41:76], r[41:76], linestyle='dotted',color='k')
ax.plot(m[78:115], r[78:115], linestyle='dotted',color='k')
common.savefig(outdir, fig, 'sizes_angular_momentum_model.pdf')
fig = plt.figure(figsize=(9.5,9.5))
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit = "$\\rm log_{10} (\\rm v_{\\rm max}/km s^{-1}$)"
xmin, xmax, ymin, ymax = 10, 15, 1.3, 3.5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
subplots = (221, 222, 223, 224)
indz = (0, 1, 2, 3)
# choose type of selection:
selec = 0 #all galaxies
# LTG ##################################
for z,s,p in zip(zlist, indz, subplots):
ax = fig.add_subplot(p)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit, locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, 'z=%s' % str(z))
ind = np.where(vmax_halo_gal[s,0,:,selec] != 0)
xplot = xmfh[ind]
yplot = vmax_halo_gal[s,0,ind,selec]
errdn = vmax_halo_gal[s,1,ind,selec]
errup = vmax_halo_gal[s,2,ind,selec]
ax.plot(xplot,yplot[0],color='k',label="central subhalos")
ax.fill_between(xplot,yplot[0],yplot[0]-errdn[0], facecolor='k', alpha=0.2,interpolate=True)
ax.fill_between(xplot,yplot[0],yplot[0]+errup[0], facecolor='k', alpha=0.2,interpolate=True)
common.prepare_legend(ax, ['k'], loc=2)
common.savefig(outdir, fig, 'vmax_vs_subhalo.pdf')
def plot_bt_resolve(plt, outdir, obsdir, ETGsmhalo, LTGsmhalo):
resolve_obs_as_errorbars = functools.partial(_resolve_obs_as_errorbars,
obsdir)
fig = plt.figure(figsize=(5, 9))
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit = "$\\rm frequency$"
xmin, xmax, ymin, ymax = 11.5, 15, -0.05, 1.05
xleg = xmax - 0.5 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
# LTG ##################################
ax = fig.add_subplot(311)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit=None,
ytit=ytit,
locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, '$\\rm all\, masses$')
#Predicted size-mass for disks
ind = np.where(ETGsmhalo[0, :] >= 0)
xplot = xmf[ind]
yplot = ETGsmhalo[0, ind]
ax.plot(xplot, yplot[0], 'r', label='SHArk ETGs')
ind = np.where(LTGsmhalo[0, :] >= 0)
xplot = xmf[ind]
yplot = LTGsmhalo[0, ind]
ax.plot(xplot, yplot[0], 'b', linestyle='dashed', label='SHArk LTGs')
#Baldry (Chabrier IMF), ['Baldry+2012, z<0.06']
resolve_obs_as_errorbars(ax,
'ETall_frac.txt', [0, 1, 2, 3],
'r',
'o',
err_absolute=False,
label='RESOLVE/ECO ETGs')
resolve_obs_as_errorbars(ax,
'LTall_frac.txt', [0, 1, 2, 3],
'b',
's',
err_absolute=False,
label='RESOLVE/ECO LTGs')
common.prepare_legend(ax, ['r', 'b', 'r', 'b'],
loc=2,
bbox_to_anchor=(0.0, 1.45))
# LTG ##################################
ax = fig.add_subplot(312)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit=None,
ytit=ytit,
locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, '$M^{\\prime}_{\\rm bar}/M_{\odot} > 10^{10}$')
#Predicted size-mass for disks
ind = np.where(ETGsmhalo[1, :] >= 0)
xplot = xmf[ind]
yplot = ETGsmhalo[1, ind]
ax.plot(xplot, yplot[0], 'r')
ind = np.where(LTGsmhalo[1, :] >= 0)
xplot = xmf[ind]
yplot = LTGsmhalo[1, ind]
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
#Baldry (Chabrier IMF), ['Baldry+2012, z<0.06']
resolve_obs_as_errorbars(ax,
'EThimbary_frac.txt', [0, 1, 2, 3],
'r',
'o',
err_absolute=False)
resolve_obs_as_errorbars(ax,
'LThimbary_frac.txt', [0, 1, 2, 3],
'b',
's',
err_absolute=False)
# LTG ##################################
ax = fig.add_subplot(313)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1, 1))
ax.text(xleg, yleg, '$M^{\\prime}_{\\rm bar}/M_{\odot} < 10^{10}$')
#Predicted size-mass for disks
ind = np.where(ETGsmhalo[2, :] >= 0)
xplot = xmf[ind]
yplot = ETGsmhalo[2, ind]
ax.plot(xplot, yplot[0], 'r')
ind = np.where(LTGsmhalo[2, :] >= 0)
xplot = xmf[ind]
yplot = LTGsmhalo[2, ind]
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
#Baldry (Chabrier IMF), ['Baldry+2012, z<0.06']
resolve_obs_as_errorbars(ax,
'ETlowmbary_frac.txt', [0, 1, 2, 3],
'r',
'o',
err_absolute=False)
resolve_obs_as_errorbars(ax,
'LTlowmbary_frac.txt', [0, 1, 2, 3],
'b',
's',
err_absolute=False)
common.savefig(outdir, fig, 'bt_resolve.pdf')
def plot_scaling_z(plt, outdir, snap, SFRMstar, R50Mstar, R50Mstar30, MBHMstar,
SigmaMstar30, ZstarMstar, ZSFMstar, AgeSMstar, SFRMstar30,
R50pMstar30, ZNSFMstar):
bin_it = functools.partial(us.wmedians, xbins=xmf)
########################### will plot main sequence for all stellar particles in the subhalo
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm SFR/M_{\odot} yr^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, -5, 1.5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, mstot, sfr, r50, ms30, sfr30 = common.load_observation(
'/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/',
'SUBFIND-EAGLE-DATABASE.data', [2, 29, 30, 26, 22, 24])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (sfr > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(sfr[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(SFRMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SFRMstar[idx, 0, ind]
errdn = SFRMstar[idx, 1, ind]
errup = SFRMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "main_sequence_z.pdf")
########################### will plot main sequence for 30kpc aperture
xtit = "$\\rm log_{10} (\\rm M_{\\star, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm SFR_{\\rm 30kpc}/M_{\odot} yr^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, -5, 1.5
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((ms30 > 0) & (sn == s) & (sfr30 > 0))
rplot = bin_it(x=np.log10(ms30[ind]), y=np.log10(sfr30[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(SFRMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SFRMstar30[idx, 0, ind]
errdn = SFRMstar30[idx, 1, ind]
errup = SFRMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "main_sequence_30kpc_z.pdf")
########################### will plot r50 vs stellar mass for all stellar particles in the subhalo
xtit = "$\\rm log_{10} (\\rm M_{\\star,tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50}/pMpc)$"
xmin, xmax, ymin, ymax = 7, 12, -3.3, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (r50 > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50[ind]) - 3.0)
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(R50Mstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50Mstar[idx, 0, ind]
errdn = R50Mstar[idx, 1, ind]
errup = R50Mstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "r50_Mstar_z.pdf")
################## will plot r50 vs stellar mass for quantities measured within 30kpc
sn, mstot, sfr, r50, r50p = common.load_observation(
'/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/',
'SUBFIND-EAGLE-DATABASE-REF.data', [2, 22, 30, 33, 34])
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc}/pMpc)$"
xmin, xmax, ymin, ymax = 7, 12, -3.3, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (r50 > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50[ind]) - 3.0)
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(R50Mstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50Mstar30[idx, 0, ind]
errdn = R50Mstar30[idx, 1, ind]
errup = R50Mstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "r50_Mstar_30kpc_z.pdf")
################## will plot r50 vs stellar mass for quantities measured within 30kpc, but in this case the r50 is projected
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm R_{\\rm 50,30kpc,2D}/pMpc)$"
xmin, xmax, ymin, ymax = 7, 12, -3.3, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (r50p > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(r50p[ind]) - 3.0)
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(R50pMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = R50pMstar30[idx, 0, ind]
errdn = R50pMstar30[idx, 1, ind]
errup = R50pMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "r50_projected_Mstar_30kpc_z.pdf")
########################### will plot stellar velocity dispersion vs. stellar mass
sn, mstot, sfr, r50, vs = common.load_observation(
'/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/',
'SUBFIND-EAGLE-DATABASE-REF.data', [2, 22, 30, 33, 25])
xtit = "$\\rm log_{10} (\\rm M_{\\star,30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\sigma_{\\star,30kpc}/km s^{-1})$"
xmin, xmax, ymin, ymax = 7, 12, 1, 3
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
plt.subplots_adjust(left=0.2)
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (vs > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(vs[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(SigmaMstar30[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = SigmaMstar30[idx, 0, ind]
errdn = SigmaMstar30[idx, 1, ind]
errup = SigmaMstar30[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "vdisp_Mstar_30kpc_z.pdf")
############ will plot stellar metallicity-stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm Z_{\star})$"
xmin, xmax, ymin, ymax = 7, 12, -5, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, mstot, zsf, zs, age, znsf = common.load_observation(
'/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/',
'SUBFIND-EAGLE-DATABASE.data', [2, 29, 33, 34, 35, 36])
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (zs > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(zs[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(ZstarMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = ZstarMstar[idx, 0, ind]
errdn = ZstarMstar[idx, 1, ind]
errup = ZstarMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "zstar_mstar_z.pdf")
################ will plot star-forming gas metallicity vs. stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm Z_{\\rm SF,gas})$"
xmin, xmax, ymin, ymax = 7, 12, -5, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (zsf > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(zsf[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(ZSFMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = ZSFMstar[idx, 0, ind]
errdn = ZSFMstar[idx, 1, ind]
errup = ZSFMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "zsfgas_mstar_z.pdf")
################ will plot non-star-forming gas metallicity vs. stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm Z_{\\rm non-SF,gas})$"
xmin, xmax, ymin, ymax = 7, 12, -5, -1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (znsf > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(znsf[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(ZNSFMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = ZNSFMstar[idx, 0, ind]
errdn = ZNSFMstar[idx, 1, ind]
errup = ZNSFMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "znsfgas_mstar_z.pdf")
################ will plot stellar ages vs stellar mass
xtit = "$\\rm log_{10} (\\rm M_{\\star, tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm age_{\\star}/Gyr)$"
xmin, xmax, ymin, ymax = 7, 12, 0, 1.1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#from SUBFIND
ind = np.where((mstot > 0) & (sn == s) & (age > 0))
rplot = bin_it(x=np.log10(mstot[ind]), y=np.log10(age[ind]))
ind = np.where(rplot[0, :] != 0.)
xplot = xmf[ind]
yplot = rplot[0, ind]
errdn = rplot[1, ind]
errup = rplot[2, ind]
if idx == 0:
ax.plot(xplot,
yplot[0],
'b',
linestyle='dashed',
label='EAGLE L25')
if idx > 0:
ax.plot(xplot, yplot[0], 'b', linestyle='dashed')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='b',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='b',
alpha=0.2,
interpolate=True)
#VR
ind = np.where(AgeSMstar[idx, 0, :] != 0.)
xplot = xmf[ind]
yplot = AgeSMstar[idx, 0, ind]
errdn = AgeSMstar[idx, 1, ind]
errup = AgeSMstar[idx, 2, ind]
if idx == 0:
ax.plot(xplot, yplot[0], 'r', label='VR')
if idx > 0:
ax.plot(xplot, yplot[0], 'r')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='r',
alpha=0.2,
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='r',
alpha=0.2,
interpolate=True)
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "starage_mstar_z.pdf")
def plot_bmf_resolve(plt, outdir, obsdir, hist_bmf, hist_bmf_sat):
load_resolve_mf_obs = functools.partial(_load_resolve_mf_obs, obsdir)
fig = plt.figure(figsize=(9.5, 10.5))
xtit = "$\\rm log_{10} (\\rm M^{\\prime}_{\\rm bar}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm dn/dM / Mpc^{-3} dex^{-1})$"
xmin, xmax, ymin, ymax = 9, 12, -6, -1
xleg = xmax - 0.3 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
# all halos ##################################
ax = fig.add_subplot(321)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit)
ax.tick_params(labelsize=13)
ax.text(xleg, yleg, '$\\rm all\, halos$')
#Predicted SMHM
ind = np.where(hist_bmf[0, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf[0, ind]
ax.errorbar(xplot, yplot[0], color='k')
ind = np.where(hist_bmf_sat[0, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf_sat[0, ind]
ax.errorbar(xplot, yplot[0], color='k', linestyle="dashed")
#RESOLVE observations
M, No, Nodn, Noup = load_resolve_mf_obs('bmassfunction_resolve.txt',
[0, 1, 2, 3])
resolve_mf_obs_as_errorbar(ax,
M,
No,
Nodn,
Noup,
'grey',
'o',
yerrdn_val=No)
M, No, Nodn, Noup = load_resolve_mf_obs('bmassfunction_eco.txt',
[0, 1, 2, 3])
eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'grey', 's', yerrdn_val=No)
# low mass halos ##################################
ax = fig.add_subplot(322)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=None)
xleg = xmax - 0.63 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
ax.text(xleg, yleg, '$11<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<11.4$')
#Predicted SMHM
ind = np.where(hist_bmf[1, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf[1, ind]
ax.errorbar(xplot, yplot[0], color='b')
ind = np.where(hist_bmf_sat[1, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf_sat[1, ind]
ax.errorbar(xplot, yplot[0], color='b', linestyle="dashed")
#RESOLVE observations
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionlowmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9])
resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'b', 'o', yerrdn_val=0.1)
resolve_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'b',
'o',
yerrdn_val=0.1,
fillstyle='full',
markersize=3)
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionlowmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9])
eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'b', 's', yerrdn_val=0.1)
eco_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'b',
's',
fillstyle='full',
markersize=3)
# medium mass halos ##################################
ax = fig.add_subplot(323)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=None, ytit=ytit)
ax.text(xleg, yleg, '$11.4<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<12$')
#Predicted SMHM
ind = np.where(hist_bmf[2, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf[2, ind]
ax.errorbar(xplot, yplot[0], color='g')
ind = np.where(hist_bmf_sat[2, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf_sat[2, ind]
ax.errorbar(xplot, yplot[0], color='g', linestyle="dashed")
#RESOLVE observations
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionmedmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9])
resolve_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'g', 'o', yerrdn_val=0.1)
resolve_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'g',
'o',
fillstyle='full',
markersize=3)
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionmedmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9])
eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'g', 's', yerrdn_val=0.1)
eco_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'g',
's',
fillstyle='full',
markersize=3)
# medium high mass halos ##################################
ax = fig.add_subplot(324)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit=xtit, ytit=None)
ax.text(xleg, yleg, '$12<\\rm log_{10}(M_{\\rm halo}/M_{\odot})<13.5$')
#Predicted SMHM
ind = np.where(hist_bmf[3, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf[3, ind]
ax.errorbar(xplot, yplot[0], color='r', label="all galaxies")
ind = np.where(hist_bmf_sat[3, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf_sat[3, ind]
ax.errorbar(xplot,
yplot[0],
color='r',
linestyle="dashed",
label="satellites")
#RESOLVE observations
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionhighmasshalos_resolve.txt', [0, 1, 2, 3, 7, 8, 9])
resolve_mf_obs_as_errorbar(ax,
M,
No,
Nodn,
Noup,
'r',
'o',
yerrdn_val=0.1,
label="RESOLVE all")
resolve_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'r',
'o',
fillstyle='full',
markersize=3,
label="RESOLVE satellites")
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionhighmasshalos_eco.txt', [0, 1, 2, 3, 7, 8, 9])
eco_mf_obs_as_errorbar(ax,
M,
No,
Nodn,
Noup,
'r',
's',
yerrdn_val=No,
label="ECO all")
eco_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'r',
's',
fillstyle='full',
markersize=3,
label="ECO satellites")
common.prepare_legend(ax, ['k', 'k', 'k', 'k', 'k', 'k'],
loc=2,
bbox_to_anchor=(0.1, -0.4))
# high mass halos ##################################
ax = fig.add_subplot(325)
common.prepare_ax(ax, xmin, xmax, ymin, ymax, xtit, ytit)
ax.text(xleg, yleg, '$\\rm log_{10}(M_{\\rm halo}/M_{\odot})>13.5$')
#Predicted SMHM
ind = np.where(hist_bmf[4, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf[4, ind]
ax.errorbar(xplot, yplot[0], color='orange')
ind = np.where(hist_bmf_sat[4, :] != 0)
xplot = xmf[ind]
yplot = hist_bmf_sat[4, ind]
ax.errorbar(xplot, yplot[0], color='orange', linestyle="dashed")
M, No, Nodn, Noup, Ns, Nsdn, Nsup = load_resolve_mf_obs(
'bmassfunctionclusterhalos_eco.txt', [0, 1, 2, 3, 7, 8, 9])
eco_mf_obs_as_errorbar(ax, M, No, Nodn, Noup, 'orange', 's', yerrdn_val=No)
eco_mf_obs_as_errorbar(ax,
M,
Ns,
Nsdn,
Nsup,
'orange',
's',
fillstyle='full',
markersize=3,
label="ECO satellites")
common.savefig(outdir, fig, 'bmf_resolve.pdf')
def plot_mHI_mhalo_resolve(plt, outdir, obsdir, mHImhalo, mHImhalo_true):
resolve_obs_as_errorbars = functools.partial(_resolve_obs_as_errorbars,
obsdir)
# Plots gas metallicity vs. stellar mass
fig = plt.figure(figsize=(5, 9))
xtit = "$\\rm log_{10} (\\rm M_{\\rm halo}/M_{\odot})$"
ytit = "$\\rm log_{10}(\\rm M_{\\rm HI}/M_{\odot})$"
xmin, xmax, ymin, ymax = 10.5, 15.2, 7, 12
xleg = xmin + 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
# centrals
ax = fig.add_subplot(211)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'centrals')
#Predicted relation
ind = np.where(mHImhalo[1, 0, :] != 0)
yplot = (mHImhalo[1, 0, ind])
errdn = (mHImhalo[1, 1, ind])
errup = (mHImhalo[1, 2, ind])
xplot = xmf[ind]
ax.plot(xplot, yplot[0], color='k', label="G/S $>0.05$")
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='grey',
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='grey',
interpolate=True)
ind = np.where(mHImhalo_true[1, 0, :] != 0)
yplot = (mHImhalo_true[1, 0, ind])
xplot = xmf[ind]
ax.errorbar(xplot,
yplot[0],
color='b',
linestyle="dashed",
label="true $M_{\\rm HI}$")
#RESOLVE observations
resolve_obs_as_errorbars(ax,
'himassvgroupmasscentral_resolve.txt',
[0, 4, 2, 6],
'orange',
'o',
label='RESOLVE')
resolve_obs_as_errorbars(ax,
'himassvgroupmasscentral_eco.txt', [0, 4, 2, 6],
'orange',
'o',
label='ECO')
common.prepare_legend(ax, ['k', 'b', 'orange', 'orange'], loc=4)
# satellites
ax = fig.add_subplot(212)
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtit,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'satellites')
#Predicted relation
ind = np.where(mHImhalo[2, 0, :] != 0)
yplot = (mHImhalo[2, 0, ind])
errdn = (mHImhalo[2, 1, ind])
errup = (mHImhalo[2, 2, ind])
xplot = xmf[ind]
ax.plot(xplot, yplot[0], color='k')
ax.fill_between(xplot,
yplot[0],
yplot[0] - errdn[0],
facecolor='grey',
interpolate=True)
ax.fill_between(xplot,
yplot[0],
yplot[0] + errup[0],
facecolor='grey',
interpolate=True)
ind = np.where(mHImhalo_true[2, 0, :] != 0)
yplot = (mHImhalo_true[2, 0, ind])
xplot = xmf[ind]
ax.errorbar(xplot, yplot[0], color='b', linestyle="dashed")
#RESOLVE observations
resolve_obs_as_errorbars(ax, 'himassvgroupmasssatellite_resolve.txt',
[0, 4, 2, 6], 'orange', 'o')
resolve_obs_as_errorbars(ax, 'himassvgroupmasssatellite_eco.txt',
[0, 4, 2, 6], 'orange', 's')
#Save figure
common.savefig(outdir, fig, 'mHI-mhalo_resolve.pdf')
def plot_mf_z(plt, outdir, snap, vol_eagle, histmtot, histm30, histmgas,
histmall):
########################### total stellar mass function
xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 12, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
sn, subgn, mstot, ms30, mgas30, mdm30, mbh30 = common.load_observation(
'/fred/oz009/clagos/EAGLE/L0025N0376/REFERENCE/data/',
'SUBFIND-EAGLE-DATABASE.data', [2, 1, 29, 22, 23, 31, 32])
mall = ms30 + mgas30 + mdm30 + mbh30
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#SMF from SUBFIND
ind = np.where((mstot > 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(mstot[ind]),
bins=np.append(mbins, mupp))
histsfof = H
y = histsfof[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],
np.log10(y[ind] / vol_eagle / dm),
'b',
label='EAGLE L25')
if idx > 0:
ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b')
#Predicted HMF
y = histmtot[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "smf_tot_z.pdf")
############################# stellar mass function (30kpc aperture)
xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 12, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#SMF from SUBFIND
ind = np.where((ms30 > 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(ms30[ind]),
bins=np.append(mbins, mupp))
histsfof = H
y = histsfof[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],
np.log10(y[ind] / vol_eagle / dm),
'b',
label='EAGLE L25')
if idx > 0:
ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b')
#Predicted HMF
y = histm30[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "smf_30kpc_z.pdf")
############################# gas mass function (30kpc aperture)
xtit = "$\\rm log_{10} (\\rm M_{\\rm gas, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm gas}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 12, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#SMF from SUBFIND
ind = np.where((mgas30 > 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(mgas30[ind]),
bins=np.append(mbins, mupp))
histsfof = H
y = histsfof[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],
np.log10(y[ind] / vol_eagle / dm),
'b',
label='EAGLE L25')
if idx > 0:
ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b')
#Predicted HMF
y = histmgas[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "gasmf_30kpc_z.pdf")
##################################### total mass function (30kpc aperture)
xtit = "$\\rm log_{10} (\\rm M_{\\rm tot, 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\rm ror}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 14, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s in zip(subplots, idx, zins, snap):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
#SMF from SUBFIND
ind = np.where((mall > 0) & (sn == s))
H, bins_edges = np.histogram(np.log10(mall[ind]),
bins=np.append(mbins, mupp))
histsfof = H
y = histsfof[:]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind],
np.log10(y[ind] / vol_eagle / dm),
'b',
label='EAGLE L25')
if idx > 0:
ax.plot(xmf[ind], np.log10(y[ind] / vol_eagle / dm), 'b')
#Predicted HMF
y = histmall[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='dashed')
if idx == 0:
common.prepare_legend(ax, ['b', 'r'])
common.savefig(outdir, fig, "allmassmf_30kpc_z.pdf")
def plot_mf_z(plt, outdir, obsdir, snap, vol_eagle, histmtot, histm30):
#construct relevant observational datasets for SMF
z0obs = []
lm, p, dpdn, dpup = common.load_observation(obsdir,
'mf/SMF/GAMAII_BBD_GSMFs.dat',
[0, 1, 2, 3])
xobs = lm
indx = np.where(p > 0)
yobs = np.log10(p[indx])
ydn = yobs - np.log10(p[indx] - dpdn[indx])
yup = np.log10(p[indx] + dpup[indx]) - yobs
z0obs.append((observation("Wright+2017",
xobs[indx],
yobs,
ydn,
yup,
err_absolute=False), 'o'))
# Moustakas (Chabrier IMF), ['Moustakas+2013, several redshifts']
zdnM13, lmM13, pM13, dp_dn_M13, dp_up_M13 = common.load_observation(
obsdir, 'mf/SMF/SMF_Moustakas2013.dat', [0, 3, 5, 6, 7])
xobsM13 = lmM13
yobsM13 = np.full(xobsM13.shape, -999.)
lerrM13 = np.full(xobsM13.shape, -999.)
herrM13 = np.full(xobsM13.shape, 999.)
indx = np.where(pM13 < 1)
yobsM13[indx] = (pM13[indx])
indx = np.where(dp_dn_M13 > 0)
lerrM13[indx] = dp_dn_M13[indx]
indx = np.where(dp_up_M13 > 0)
herrM13[indx] = dp_up_M13[indx]
# Muzzin (Kroupa IMF), ['Moustakas+2013, several redshifts']
zdnMu13, zupMu13, lmMu13, pMu13, dp_dn_Mu13, dp_up_Mu13 = common.load_observation(
obsdir, 'mf/SMF/SMF_Muzzin2013.dat', [0, 1, 2, 4, 5, 5])
# -0.09 corresponds to the IMF correction
xobsMu13 = lmMu13 - 0.09
yobsMu13 = np.full(xobsMu13.shape, -999.)
lerrMu13 = np.full(xobsMu13.shape, -999.)
herrMu13 = np.full(xobsMu13.shape, 999.)
indx = np.where(pMu13 < 1)
yobsMu13[indx] = (pMu13[indx])
indx = np.where(dp_dn_Mu13 > 0)
lerrMu13[indx] = dp_dn_Mu13[indx]
indx = np.where(dp_up_Mu13 > 0)
herrMu13[indx] = dp_up_Mu13[indx]
# Santini 2012 (Salpeter IMF)
zdnS12, lmS12, pS12, dp_dn_S12, dp_up_S12 = common.load_observation(
obsdir, 'mf/SMF/SMF_Santini2012.dat', [0, 2, 3, 4, 5])
hobs = 0.7
# factor 0.24 corresponds to the IMF correction.
xobsS12 = lmS12 - 0.24 + np.log10(hobs / h0)
yobsS12 = np.full(xobsS12.shape, -999.)
lerrS12 = np.full(xobsS12.shape, -999.)
herrS12 = np.full(xobsS12.shape, 999.)
indx = np.where(pS12 < 1)
yobsS12[indx] = (pS12[indx]) + np.log10(pow(h0 / hobs, 3.0))
indx = np.where(dp_dn_S12 > 0)
lerrS12[indx] = dp_dn_S12[indx]
indx = np.where(dp_up_S12 > 0)
herrS12[indx] = dp_up_S12[indx]
# Wright et al. (2018, several reshifts). Assumes Chabrier IMF.
zD17, lmD17, pD17, dp_dn_D17, dp_up_D17 = common.load_observation(
obsdir, 'mf/SMF/Wright18_CombinedSMF.dat', [0, 1, 2, 3, 4])
hobs = 0.7
pD17 = pD17 - 3.0 * np.log10(hobs)
lmD17 = lmD17 - np.log10(hobs)
# z0.5 obs
z05obs = []
in_redshift = np.where(zdnM13 == 0.4)
z05obs.append((observation("Moustakas+2013",
xobsM13[in_redshift],
yobsM13[in_redshift],
lerrM13[in_redshift],
herrM13[in_redshift],
err_absolute=False), 'o'))
in_redshift = np.where(zdnMu13 == 0.5)
z05obs.append((observation("Muzzin+2013",
xobsMu13[in_redshift],
yobsMu13[in_redshift],
lerrMu13[in_redshift],
herrMu13[in_redshift],
err_absolute=False), '+'))
in_redshift = np.where(zD17 == 0.5)
z05obs.append((observation("Wright+2018",
lmD17[in_redshift],
pD17[in_redshift],
dp_dn_D17[in_redshift],
dp_up_D17[in_redshift],
err_absolute=False), 'D'))
# z1 obs
z1obs = []
in_redshift = np.where(zdnM13 == 0.8)
z1obs.append((observation("Moustakas+2013",
xobsM13[in_redshift],
yobsM13[in_redshift],
lerrM13[in_redshift],
herrM13[in_redshift],
err_absolute=False), 'o'))
in_redshift = np.where(zdnMu13 == 1)
z1obs.append((observation("Muzzin+2013",
xobsMu13[in_redshift],
yobsMu13[in_redshift],
lerrMu13[in_redshift],
herrMu13[in_redshift],
err_absolute=False), '+'))
in_redshift = np.where(zD17 == 1)
z1obs.append((observation("Wright+2018",
lmD17[in_redshift],
pD17[in_redshift],
dp_dn_D17[in_redshift],
dp_up_D17[in_redshift],
err_absolute=False), 'D'))
#z2 obs
z2obs = []
in_redshift = np.where(zupMu13 == 2.5)
z2obs.append((observation("Muzzin+2013",
xobsMu13[in_redshift],
yobsMu13[in_redshift],
lerrMu13[in_redshift],
herrMu13[in_redshift],
err_absolute=False), '+'))
in_redshift = np.where(zdnS12 == 1.8)
z2obs.append((observation("Santini+2012",
xobsS12[in_redshift],
yobsS12[in_redshift],
lerrS12[in_redshift],
herrS12[in_redshift],
err_absolute=False), 'o'))
in_redshift = np.where(zD17 == 2)
z2obs.append((observation("Wright+2018",
lmD17[in_redshift],
pD17[in_redshift],
dp_dn_D17[in_redshift],
dp_up_D17[in_redshift],
err_absolute=False), 'D'))
# z3 obs
z3obs = []
in_redshift = np.where(zupMu13 == 3.0)
z3obs.append((observation("Muzzin+2013",
xobsMu13[in_redshift],
yobsMu13[in_redshift],
lerrMu13[in_redshift],
herrMu13[in_redshift],
err_absolute=False), '+'))
in_redshift = np.where(zdnS12 == 2.5)
z3obs.append((observation("Santini+2012",
xobsS12[in_redshift],
yobsS12[in_redshift],
lerrS12[in_redshift],
herrS12[in_redshift],
err_absolute=False), 'o'))
in_redshift = np.where(zD17 == 3)
z3obs.append((observation("Wright+2018",
lmD17[in_redshift],
pD17[in_redshift],
dp_dn_D17[in_redshift],
dp_up_D17[in_redshift],
err_absolute=False), 'D'))
# z4 obs
z4obs = []
in_redshift = np.where(zupMu13 == 4.0)
z4obs.append((observation("Muzzin+2013",
xobsMu13[in_redshift],
yobsMu13[in_redshift],
lerrMu13[in_redshift],
herrMu13[in_redshift],
err_absolute=False), '+'))
in_redshift = np.where(zdnS12 == 3.5)
z4obs.append((observation("Santini+2012",
xobsS12[in_redshift],
yobsS12[in_redshift],
lerrS12[in_redshift],
herrS12[in_redshift],
err_absolute=False), 'o'))
in_redshift = np.where(zD17 == 4)
z4obs.append((observation("Wright+2018",
lmD17[in_redshift],
pD17[in_redshift],
dp_dn_D17[in_redshift],
dp_up_D17[in_redshift],
err_absolute=False), 'D'))
########################### total stellar mass function
xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm tot}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 12, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
observations = (z0obs, z1obs, z2obs)
for subplot, idx, z, s, obs_and_markers in zip(subplots, idx, zins, snap,
observations):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
# Observations
for obs, marker in obs_and_markers:
common.errorbars(ax,
obs.x,
obs.y,
obs.yerrdn,
obs.yerrup,
'grey',
marker,
err_absolute=obs.err_absolute,
label=obs.label)
#Predicted HMF
y = histmtot[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='solid', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='solid')
if idx == 0:
cols = ['r'] + ['grey', 'grey', 'grey']
common.prepare_legend(ax, cols)
common.savefig(outdir, fig, "smf_tot_z_comp_obs.pdf")
############################# stellar mass function (30kpc aperture)
xtit = "$\\rm log_{10} (\\rm M_{\\star,\\rm 30kpc}/M_{\odot})$"
ytit = "$\\rm log_{10}(\Phi/dlog{\\rm M_{\\star}}/{\\rm Mpc}^{-3} )$"
xmin, xmax, ymin, ymax = 7, 12, -6, 1
xleg = xmax - 0.2 * (xmax - xmin)
yleg = ymax - 0.1 * (ymax - ymin)
fig = plt.figure(figsize=(5, 10))
idx = [0, 1, 2]
zins = [0, 1, 2]
subplots = [311, 312, 313]
for subplot, idx, z, s, obs_and_markers in zip(subplots, idx, zins, snap,
observations):
ax = fig.add_subplot(subplot)
if (idx == 2):
xtitplot = xtit
else:
xtitplot = ' '
common.prepare_ax(ax,
xmin,
xmax,
ymin,
ymax,
xtitplot,
ytit,
locators=(0.1, 1, 0.1))
ax.text(xleg, yleg, 'z=%s' % (str(z)))
# Observations
for obs, marker in obs_and_markers:
common.errorbars(ax,
obs.x,
obs.y,
obs.yerrdn,
obs.yerrup,
'grey',
marker,
err_absolute=obs.err_absolute,
label=obs.label)
#Predicted HMF
y = histm30[idx, :]
ind = np.where(y != 0.)
if idx == 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='solid', label='VR')
if idx > 0:
ax.plot(xmf[ind], y[ind], 'r', linestyle='solid')
if idx == 0:
cols = ['r'] + ['grey', 'grey', 'grey']
common.prepare_legend(ax, cols)
else:
cols = ['grey', 'grey', 'grey']
common.prepare_legend(ax, cols)
common.savefig(outdir, fig, "smf_30kpc_z_comp_obs.pdf")
