for ii, tag in enumerate(tags):
z = float(tag[5:].replace('p','.'))
print (z)
df = pd.read_csv('Magnitude_limits.txt')
low = np.array(df[filters])[ii]
Halpha = np.concatenate(get_line_all(tag, 'HI6563', inp = 'FLARES', LF = False)[:,1])
Hbeta = np.concatenate(get_line_all(tag, 'HI4861', inp = 'FLARES', LF = False)[:,1])
CIII = np.concatenate(get_line_all(tag, 'CIII1907', inp = 'FLARES', LF = False)[:,1] + get_line_all(tag, 'CIII1909', inp = 'FLARES', LF = False)[:,1])
OII = np.concatenate(get_line_all(tag, 'OII3726', inp = 'FLARES', LF = False)[:,1] + get_line_all(tag, 'OII3729', inp = 'FLARES', LF = False)[:,1])
# NeIII = get_line_all(tag, 'NeIII3869', inp = 'FLARES', LF = False) + get_line_all(tag, 'NeIII3967', inp = 'FLARES', LF = False)
OIII = np.concatenate(get_line_all(tag, 'OIII4959', inp = 'FLARES', LF = False)[:,1] + get_line_all(tag, 'OIII5007', inp = 'FLARES', LF = False)[:,1])
if input == plt_options[1]:
x_inp = get_data_all(tag, dataset = 'Mstar_30', DF=False)*1e10
for kk in range(len(x_inp)): x_inp[kk] = np.log10(x_inp[kk])
bins = np.arange(7.5, 11.5, 0.5)
xlabel=r'log$_{10}$(M$_{\star}$/M$_{\odot}$)'
xpos = 0.455
bottom = 0.20
elif input == plt_options[2]:
x_inp = get_lum_all(tag, LF=False)
for kk in range(len(x_inp)): x_inp[kk] = lum_to_M(x_inp[kk])
bins = -np.arange(17, 25, 1)[::-1]
xlabel = r'$\mathrm{M}_{1500}$'
xpos = 0.47
bottom = 0.21
for kk in range(5): axs[kk].set_xlim((-17,-23.9))
edgecolor='k')
axs = axs.ravel()
for ii, tag in enumerate(tags):
df = pd.read_csv('Magnitude_limits.txt')
low = np.array(df[filters])[ii]
bins = -np.arange(-low, 25, 0.4)[::-1]
L_FUV = get_lum_all(tag, LF=False, filter='FUV', Luminosity='DustModelI')
L_NUV = get_lum_all(tag, LF=False, filter='NUV', Luminosity='DustModelI')
L_FUV_int = get_lum_all(tag,
LF=False,
filter='FUV',
Luminosity='Intrinsic')
Mstar_30 = get_data_all(tag, inp='FLARES', DF=False)
sfr_30 = get_data_all(tag, dataset='SFR_inst_30', inp='FLARES', DF=False)
ws = np.array([])
for jj in range(len(weights)):
ws = np.append(ws, np.ones(np.shape(L_FUV[jj])) * weights[jj])
L_FUV = np.concatenate(L_FUV)
L_FUV_int = np.concatenate(L_FUV_int)
L_NUV = np.concatenate(L_NUV)
Mstar_30 = np.concatenate(Mstar_30) * 1e10
sfr_30 = np.concatenate(sfr_30)
ok = np.where(lum_to_M(L_FUV) < low)[0]
L_FUV, L_NUV, L_FUV_int, Mstar_30 = L_FUV[ok], L_NUV[ok], L_FUV_int[
ok], Mstar_30[ok]
sfr_30 = sfr_30[ok] / Mstar_30
weights = np.array(df['weights'])
sims = np.arange(len(weights))
fig, axs = plt.subplots(nrows = 2, ncols = 3, figsize=(13, 6), sharex=False, sharey=False, facecolor='w', edgecolor='k')
axs = axs.ravel()
for ii, tag in enumerate(tags):
z = float(tag[5:].replace('p','.'))
print (z)
dat1 = get_line_all(tag, 'OIII4959', inp = 'FLARES', LF = False)
dat2 = get_line_all(tag, 'OIII5007', inp = 'FLARES', LF = False)
dat3 = get_line_all(tag, 'HI4861', inp = 'FLARES', LF = False)
l_fuvs = np.concatenate(get_lum_all(tag, LF=False))
mstar = get_data_all(tag, dataset = 'Mstar_30', DF=False)
ws = np.array([])
for jj in sims:
ws = np.append(ws, np.ones(np.shape(mstar[jj]))*weights[jj])
mstar = np.concatenate(mstar)*1e10
OIII4959_lum = np.concatenate(dat1[:,0])
OIII4959_EW = np.concatenate(dat1[:,1])
OIII5007_lum = np.concatenate(dat2[:,0])
OIII5007_EW = np.concatenate(dat2[:,1])
Hbeta_lum = np.concatenate(dat3[:,0])
Hbeta_EW = np.concatenate(dat3[:,1])
c_m = matplotlib.cm.viridis_r
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
bins = np.arange(-1, 4, 0.4)
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
for ii, tag in enumerate(tags):
z = float(tag[5:].replace('p', '.'))
axs[ii].text(-0.5, -5.7, r'$z = {}$'.format(z), fontsize=12)
sfr_30 = get_data_all(tag,
dataset='SFR/SFR_100',
inp='FLARES',
DF=False)
L_FUV = get_lum_all(tag,
LF=False,
filter='FUV',
Luminosity='DustModelI')
L_FUV_int = get_lum_all(tag,
LF=False,
filter='FUV',
Luminosity='Intrinsic')
sfr_tot = np.zeros(len(bincen))
sfr_tot_err = np.zeros(len(bincen))
sfr_obsc = np.zeros(len(bincen))
sfr_unobsc = np.zeros(len(bincen))
norm = matplotlib.colors.Normalize(vmin=0.5, vmax=len(tags)+0.5)
# choose a colormap
c_m = matplotlib.cm.viridis_r
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
bins = np.arange(7.5, 12, 0.5)
bincen = (bins[1:]+bins[:-1])/2.
yerr1_lo, yerr1_up, yerr2_lo, yerr2_up = np.array([]), np.array([]), np.array([]), np.array([])
for ii, tag in enumerate(tags):
z = float(tag[5:].replace('p','.'))
mstar = get_data_all(tag, inp = 'FLARES', DF = False)
ws = np.array([])
for jj in sims:
ws = np.append(ws, np.ones(np.shape(mstar[jj]))*weights[jj])
mstar = np.log10(np.concatenate(mstar)*1e10)
data = get_Z(tag)
S_met = np.log10(np.concatenate(data[:,0]))
G_met = np.log10(np.concatenate(data[:,1]))
out = flares.binned_weighted_quantile(mstar, G_met,ws,bins,quantiles)
xx, yy, yy84, yy16 = bincen, out[:,1], out[:,0],out[:,2]
hist, edges=np.histogram(mstar, bins)
ok = np.where(hist>=5)[0]
ax.errorbar(xx[ok], yy[ok], ls='-', color=s_m.to_rgba(ii+0.5), alpha=.9, lw=1, fmt='s')
# choose a colormap
c_m = matplotlib.cm.viridis_r
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
for ii, tag in enumerate(tags):
axs[ii].text(7.9, -6.5, r'$z = {}$'.format(zs[ii]), fontsize=10)
bins = np.arange(7.5, 12.5, 0.25)
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
out, hist, err = get_data_all(tag, bins=bins, inp='FLARES', DF=True)
ok = np.where(hist > 0)[0]
hist = hist[ok]
Msim = out / (binwidth * vol)
xerr = np.ones(len(out)) * binwidth[0] / 2.
yerr = err / (vol * binwidth)
ok1 = np.where(hist >= 5)[0][-1]
Mref = get_data_all(tags_ref[ii], bins=bins, inp='REF',
DF=True) / (binwidth * refvol)
okref = np.where(Mref > 0)
# Magn = get_data_all(tags_ref[ii], bins = bins, inp = 'AGNdT9', DF = True)/(binwidth*AGNdT9vol)
# okagn = np.where(Magn>0)
# if zs[ii]>=8: