def get_mhalo_mstar_fit(sim, mstar_min=9., order=1):
gal_mstar = np.log10(
np.array([i.masses['stellar'].in_units('Msun') for i in sim.galaxies]))
gal_mhalo = np.log10(
np.array(
[i.halo.masses['total'].in_units('Msun') for i in sim.galaxies]))
gal_cent = np.array([i.central for i in sim.galaxies])
mstar_mask = gal_mstar > mstar_min
mhalo_bins, median, lower, upper, ngals = runningmedian(
gal_mhalo[gal_cent * mstar_mask], gal_mstar[gal_cent * mstar_mask])
mhalo_bins = mhalo_bins[~np.isnan(median)]
lower = lower[~np.isnan(median)]
upper = upper[~np.isnan(median)]
median = median[~np.isnan(median)]
dof = len(mhalo_bins) - order
coeffs = np.polyfit(mhalo_bins, median, order)
model_vals = np.polyval(coeffs, mhalo_bins)
chi_r = reduced_chi_squared(median, model_vals, np.minimum(lower, upper),
dof)
print(f'Reduced Chi Squared = {chi_r}')
print(f'Coefficients: {coeffs}')
plot_model_fit(mhalo_bins, median, lower, upper, model_vals,
np.round(coeffs, 4),
f'plots/{model}_{wind}_{snap}_mhalo_mstar_deg_{order}.png')
return coeffs
print('%d logM*= %.3f c= [%.3f,%.3f] rh2d= %.3f %.3f %.3f rh3d= %.3f' % \
(igal,np.log10(ms[igal]),cent[0],cent[1],rhalf_l[0][igal],rhalf_l[1][igal],rhalf_l[2][igal],mygals[igal].radii['stellar_half_mass']))
del mass, age, sZ, pos, lum
gc.collect()
#print len(ms),len(sigv3d),len(sigmastar)
rad_l = (rhalf_l[0] + rhalf_l[1] + rhalf_l[2]) / 3.
rad_m = (rhalf_m[0] + rhalf_m[1] + rhalf_m[2]) / 3.
with h5py.File(
data_dir + MODEL + '_' + WIND[0] + '_' + str(SNAP) +
'_data.h5', 'w') as hf:
hf.create_dataset('halflight', data=np.array(rad_l))
hf.create_dataset('halfmass', data=np.array(rad_m))
condition = (central)
massbin, cvecbin, ebinlo, ebinhi = pm.runningmedian(
np.log10(ms[condition]), ssfr[condition])
cvec = ssfr - np.interp(np.log10(ms), massbin, cvecbin)
cmap = plt.get_cmap('jet_r')
# plot half light radius against stellar mass
print 'Plotting half light radius against stellar mass'
fig, ax = plt.subplots()
xvec = np.log10(ms[rad_l > 0])
yvec = np.log10(rad_l[rad_l > 0])
cvec = cvec[rad_l > 0]
pixsize = 3 * (xvec / min(xvec) + 1)
im = ax.scatter(xvec,
yvec,
c=cvec,
area = get_area(dr, n)
area_phys = gal_rad_tile**2. * area
area_total = np.sum(area_phys[:, :n], axis=1)
h1_mass = h1_surf_dens[:, :n] * area_phys
h2_mass = h2_surf_dens[:, :n] * area_phys
sfr = sfr_surf_dens[:, :n] * area_phys
gas_mass_total = np.sum(h1_mass, axis=1) + np.sum(h2_mass, axis=1)
sigma_h2 = np.log10(np.sum(h2_mass, axis=1) / area_total) - 6.
sigma_gas = np.log10(gas_mass_total / area_total) - 6.
sfr_total = np.sum(sfr, axis=1)
sigma_sfr = np.log10((sfr_total / area_total) + 10**-4.5)
condition = (sigma_gas > 0.)
bin_cent,ymean,ysiglo,ysighi = pm.runningmedian(sigma_gas[condition], sigma_sfr[condition], stat='mean')
fig, ax = plt.subplots(figsize=(5, 4))
if snap == '151':
plot_kennicutt(ax, labels=[r'$\Sigma_{\textrm{SFR}} \propto \Sigma_{\textrm{HI} + \textrm{H}_2}^{1.4}$'])
ax.plot(bigiel_contour_1[0], bigiel_contour_1[1] * bigiel_imf_factor, c='k', lw=1, label='Bigiel+08')
ax.plot(bigiel_contour_2[0], bigiel_contour_2[1] * bigiel_imf_factor, c='k', lw=1)
ax.plot(bigiel_contour_3[0], bigiel_contour_3[1] * bigiel_imf_factor, c='k', lw=1)
ax.plot(bigiel_contour_4[0], bigiel_contour_4[1] * bigiel_imf_factor, c='k', lw=1)
#ax.scatter(spirals_kennicutt_98[0], spirals_kennicutt_98[1] * bigiel_imf_factor, c='k', marker='o', s=10, label='Spirals, Kennicutt 98')
#ax.scatter(m51_kennicutt_07[0], m51_kennicutt_07[1], c='k', marker='+', s=14, label='M51, Kennicutt+07')
ax.plot(bin_cent, ymean, c='dodgerblue', lw=2.5, ls='--', label=r'$\textrm{HI} + \textrm{H}_2$')
im = ax.scatter(sigma_gas[gal_ssfr>-1.8], sigma_sfr[gal_ssfr> -1.8], c=gal_ssfr[gal_ssfr>-1.8], s=0.5, cmap =new_cmap)
cbar = fig.colorbar(im,ax=ax, label=r'$ \textrm{log} (sSFR / Gyr^{-1})$')
f.create_dataset('stellar_mass', data=np.array(ms))
f.create_dataset('sfr', data=np.array(sfr))
else:
with h5py.File(caesar_data, 'r') as f:
central = f['central'][:]
ms = f['stellar_mass'][:]
sfr = f['sfr'][:]
ssfr = np.log10(1.e9 * sfr / ms)
ssfrlim = -1.8 + 0.3 * simba_z
with h5py.File(rhalf_file, 'r') as r:
rhalf = r[mtype + '_' + model + '_' + wind + '_' + snap][:]
rhalf = np.sum(rhalf, axis=0) / 3.
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(
np.log10(ms[ssfr > ssfrlim]), np.log10(rhalf[ssfr > ssfrlim]))
ax.plot(bin_cent, ymean, lines[i], lw=2, color='blue')
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(
np.log10(ms[ssfr < ssfrlim]), np.log10(rhalf[ssfr < ssfrlim]))
ax.plot(bin_cent, ymean, lines[i], lw=2, color='m')
res_line = np.log10(2.8 * 0.5 / ((1 + simba_z) * 0.68))
ax.axhline(
res_line,
linestyle='--',
color='k',
lw=1.5,
)
plt.annotate('z=%g' % (np.round(simba_z, 1)),
xy=(0.05, 0.93),
xycoords='axes fraction',
sfr = f['sfr'].value
if simba_z == 0.:
rhalf_file = '/home/sapple/simba_sizes/sizes/data/pyloser_sdss_r.h5'
else:
rhalf_file = '/home/sapple/simba_sizes/sizes/data/pyloser_v.h5'
with h5py.File(rhalf_file, 'r') as r:
rhalf = r[mtype+'_'+model+'_'+wind+'_'+snap].value
rhalf = np.sum(rhalf, axis=0) / 3
ssfr = 1.e9*sfr/ms
ssfr = np.log10(ssfr)
ssfrlim = -1.8+0.3*simba_z
# plot simba galaxy points
massbin,cvecbin,ebinlo,ebinhi = pm.runningmedian(np.log10(ms[central]),ssfr[central])
simba_x = np.log10(ms[rhalf>0])
simba_y = np.log10(rhalf[rhalf>0])
simba_c = ssfr - np.interp(np.log10(ms),massbin,cvecbin)
simba_c = ssfr
simba_c[simba_c < -2.5] = -2.5
simba_c[simba_c > 1.] = 1.
pixsize = 1*(simba_x-min(simba_x))+0.5
im = ax.scatter(simba_x, simba_y, c=simba_c, s=pixsize, lw=0, cmap=cmap)
# plot simba red and blue/ all galaxies
if simba_z <= 2.5:
bin_cent,ymean,ysiglo,ysighi = pm.runningmedian(np.log10(ms[ssfr>ssfrlim]),np.log10(rhalf[ssfr>ssfrlim]))
ax.plot(bin_cent,ymean,'--',lw=4,color='dodgerblue', label='Star forming')
plt.axhline(1., linestyle='--', color='m')
plt.ylabel(r'$\textrm{HI surface density}$')
plt.xlabel(r'$R (\textrm{kpc})$')
plt.title(title)
plt.xlim(0, )
plt.savefig(results_dir + '/profiles/h1_profile_gal_' + str(gal_ids[i]) +
'.png')
plt.clf()
with h5py.File(
results_dir + '/' + model + '_' + wind + '_' + snap + '_h1_data.h5',
'a') as f:
f.create_dataset('gal_ids', data=np.array(gal_ids))
f.create_dataset('h1_radii', data=np.array(h1_radii))
f.create_dataset('h1_mass', data=np.array(gal_h1m[mask]))
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(np.log10(gal_h1m[mask]),
h1_radii)
plt.scatter(np.log10(gal_h1m[mask]),
h1_radii,
c=richness[mask],
s=1.,
cmap='jet_r')
plt.colorbar(label=r'$\textrm{log} (M_{HI} / M_*)$')
plt.plot(bin_cent, ymean, '--', lw=3, color='g')
plt.xlabel(r'$\textrm{log} (M_{HI} / M_{\odot})$')
plt.ylabel(r'$R_{HI} (\textrm{kpc})$')
plt.savefig(results_dir + '/' + model + '_' + wind + '_' + snap +
'_h1_size_mass.png')
plt.clf()
pixsize = 1 * (np.log10(ms) - min(np.log10(ms))) + 0.5
ax = fig.add_subplot(3, 2, i + 1)
im = ax.scatter(np.log10(ms),
conc,
c=simba_c,
s=pixsize,
lw=0,
cmap=cmap,
label='Simba')
cbar = fig.colorbar(
im, ax=ax, label=r'$\textrm{log} (\textrm{sSFR} / \textrm{Gyr}^{-1})$')
cbar.ax.tick_params(labelsize=10)
if simba_z[i] <= 1.5:
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(
np.log10(ms[ssfr > ssfrlim]), conc[ssfr > ssfrlim])
ax.plot(bin_cent, ymean, '--', lw=4, color='c', label='Star forming')
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(
np.log10(ms[ssfr < ssfrlim]), conc[ssfr < ssfrlim])
ax.plot(bin_cent, ymean, '--', lw=4, color='m', label='Passive')
else:
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(np.log10(ms), conc)
ax.plot(bin_cent, ymean, '--', lw=4, color='b', label='Simba')
ax.annotate('z=%g' % (np.round(simba_z[i], 1)),
xy=(0.1, 0.9),
xycoords='axes fraction',
size=16,
bbox=dict(boxstyle="round", fc="w"))
ax.minorticks_on()
#print len(ms),len(sigv3d),len(sigmastar)
logms = np.log10(ms)
logmbh = np.log10(mbh)
logsig = np.log10(np.asarray(sigv3d))
logms_sat = np.log10(ms_sat)
logmbh_sat = np.log10(mbh_sat)
logsig_sat = np.log10(sigmastar)
Zmet = np.log10(met/0.0189+1.e-6)
ssfr = 1.e9*sfr/ms
ssfr = np.log10(ssfr+10**(-2.5+0.3*redshift))
pixcolor = np.log10(rad)
pixsize = 4*(np.log10(ms/min(ms))+1)
cvec = np.log10(rad)
cvec_sat = np.log10(rad_sat)
massbin,cvecbin,ebinlo,ebinhi = pm.runningmedian(np.log10(ms[sfr>0]),np.log10(rad[sfr>0]))
cvec = cvec - np.interp(np.log10(ms),massbin,cvecbin)
cvec_sat = cvec_sat - np.interp(np.log10(ms_sat),massbin,cvecbin)
if iwind==0:
#ax.plot(logms,logsig_sat, 'x', c='grey', ms=1, label='Sats')
im = ax.scatter(logms,logsig, c=cvec, s=pixsize, lw=0, cmap=plt.cm.jet_r, label='Centrals')
fig.colorbar(im,ax=ax,label=r'$\Delta r_*$ (kpc)')
im.set_clim(-0.2,0.2)
else: im = ax.scatter(logms_sat, logsig_sat, c='k', s=pixsize, lw=0)
#plt.plot(logms,Zmet,ms=1,lw=0,c='r')
#print logms,Zmet
#plot_data(0)
#plot_data(0,'.')
with h5py.File(profs_file, 'r') as f:
h1_h2_m = np.log10(f['h2_m'].value[mask] +
f['h1_m'].value[mask]) - 6. # go from kpc to pc
sfr = np.log10(f['sfr'].value[mask] + 1.e-6)
n = sfr.shape[1]
dr = 0.2
factor = dr * n
bins = np.arange(0., factor, dr)
rplot = bins + (dr * 0.5)
rplot = np.tile(rplot, len(sfr))
h1_h2_m = h1_h2_m.flatten()
sfr = sfr.flatten()
bin_cent, ymean, ysiglo, ysighi = pm.runningmedian(h1_h2_m[h1_h2_m > -1],
sfr[h1_h2_m > -1],
stat='mean')
fig, ax = plt.subplots()
plot_kennicutt(ax, labels=['Kennicutt 1998', 'Bigiel 2008'])
ax.plot(bin_cent, ymean, c='m', lw=2.5, ls='--')
im = ax.scatter(h1_h2_m.flatten(),
sfr.flatten(),
c=rplot,
s=0.5,
cmap=new_cmap)
cbar = fig.colorbar(im, ax=ax, label=r'$ R / R_{half}$')
ax.set_xlabel(
r'$ \textrm{log} (\Sigma _{HI + H2}/ M_{\odot}\textrm{pc}^{-2}) $')
ax.set_ylabel(
r'$ \textrm{log} (\Sigma _{\textrm{SFR}}/ M_{\odot}\textrm{yr}^{-1} \textrm{kpc}^{-2}) $'