def fit(ii, tag, bins, model):
print('tag:', tag)
V = (3200)**3
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
out, hist_all, err = get_lum_all(tags[ii], bins=bins)
ok = np.where(hist_all <= 5)[0]
out[ok] = 0.
hist_all[ok] = 0.
err[ok] = 0.
phi = out / (vol * binwidth)
err = err / (vol * binwidth)
N = models.phi_to_N(phi, V, bins)
samples = fitdf(ii, tag, N, hist_all, V, bins, model)
return None
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 = get_line_all(tag, 'HI6563', inp = 'FLARES', LF = False)
Hbeta = get_line_all(tag, 'HI4861', inp = 'FLARES', LF = False)
CIII = get_line_all(tag, 'CIII1907', inp = 'FLARES', LF = False) + get_line_all(tag, 'CIII1909', inp = 'FLARES', LF = False)
OII = get_line_all(tag, 'OII3726', inp = 'FLARES', LF = False) + get_line_all(tag, 'OII3729', inp = 'FLARES', LF = False)
# NeIII = get_line_all(tag, 'NeIII3869', inp = 'FLARES', LF = False) + get_line_all(tag, 'NeIII3967', inp = 'FLARES', LF = False)
OIII = get_line_all(tag, 'OIII4959', inp = 'FLARES', LF = False) + get_line_all(tag, 'OIII5007', inp = 'FLARES', LF = False)
l_fuvs = np.concatenate(get_lum_all(tag, LF=False))
LF_create(Halpha, weights, l_fuvs, low, axs[0], titles[0], s_m.to_rgba(ii+0.5))
LF_create(Hbeta, weights, l_fuvs, low, axs[1], titles[1], s_m.to_rgba(ii+0.5))
LF_create(CIII, weights, l_fuvs, low, axs[2], titles[2], s_m.to_rgba(ii+0.5))
LF_create(OII, weights, l_fuvs, low, axs[3], titles[3], s_m.to_rgba(ii+0.5))
# LF_create(NeIII, weights, l_fuvs, low, axs[4], titles[4], s_m.to_rgba(ii+0.5))
LF_create(OIII, weights, l_fuvs, low, axs[4], titles[4], s_m.to_rgba(ii+0.5))
for ii in range(5):
axs[ii].set_xlim(xlims[ii])
axs[ii].set_ylim(-9.,-1.8)
for label in (axs[ii].get_xticklabels() + axs[ii].get_yticklabels()):
label.set_fontsize(13)
fig, axs = plt.subplots(nrows=2,
ncols=3,
figsize=(13, 5),
sharex=True,
sharey=True,
facecolor='w',
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
# create a ScalarMappable and initialize a data structure
s_m = matplotlib.cm.ScalarMappable(cmap=c_m, norm=norm)
s_m.set_array([])
dat = pd.DataFrame({})
for ii, z in enumerate(zs):
df = pd.read_csv('Magnitude_limits.txt')
low = np.array(df[filters])[ii]
bins = -np.arange(-low, 26.5, 0.5)[::-1]
bincen = (bins[1:]+bins[:-1])/2.
binwidth = bins[1:] - bins[:-1]
print ("\n tag: ", tags[ii])
out, hist, err = get_lum_all(tags[ii], bins=bins)
Msim = out/(binwidth*vol)
xerr = np.ones(len(out))*binwidth[0]/2.
yerr = err/(vol*binwidth)
ok = np.where(hist > 0)[0]
observed = Msim*(binwidth*parent_volume)
sigma = observed/np.sqrt(hist)
xx, yy, zz, number = np.zeros(15), np.zeros(15), np.zeros(15), np.zeros(15)
num = len(ok)
xx[:num], yy[:num], zz[:num], number[:num] = bincen[ok], Msim[ok], yerr[ok], hist[ok]
dat[f"M{z}"] = xx
dat[f"phi{z}"] = yy
dat[F"err{z}"] = zz
dat[F"num{z}"] = number
c = 'forestgreen'
ax.scatter(o['L1500']+np.log10(conversion_fac),np.log10(o['EW'][0]), marker=marker, s = 15, c = c, zorder = 5, label = r'$\rm '+id+'\ ('+o['ref']+')\ z='+str(o['z'])+'$')
if o['EW'][1]>0:
ax.plot([o['L1500']+np.log10(conversion_fac)]*2, [np.log10(o['EW'][0]-o['EW'][1]),np.log10(o['EW'][0]+o['EW'][1])], c = c, lw = 1)
else:
ax.arrow(o['L1500']+np.log10(conversion_fac), np.log10(o['EW'][0]), 0.0, -0.07, color=c, head_width=0.05, head_length=0.01)
dat1 = get_line_all(tag, 'CIII1907', inp = 'FLARES', LF = False, Type = 'Intrinsic')
dat2 = get_line_all(tag, 'CIII1909', inp = 'FLARES', LF = False, Type = 'Intrinsic')
l_fuvs = get_lum_all(tag, LF=False)
ws = np.array([])
for jj in sims:
ws = np.append(ws, np.ones(np.shape(l_fuvs[jj]))*weights[jj])
l_fuvs = np.concatenate(l_fuvs)*conversion_fac
CIII1907_lum = np.concatenate(dat1[:,0])
CIII1907_EW = np.concatenate(dat1[:,1])
CIII1909_lum = np.concatenate(dat2[:,0])
CIII1909_EW = np.concatenate(dat2[:,1])
x, y, w = np.log10(l_fuvs), CIII1907_EW+CIII1909_EW, ws
thisok = np.where(y>0)
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
if rank == 0:
hists = np.zeros((len(facs),len(bincen)))
hist = np.zeros((len(facs),len(bincen)))
else:
hists = np.empty((len(facs),len(bincen)))
hist = np.zeros((len(facs),len(bincen)))
part = int(len(facs)/size)
if rank!=size-1:
thisok = np.arange(rank*part, (rank+1)*part, 1).astype(int)
else:
thisok = np.arange(rank*part, len(facs), 1).astype(int)
for ii, jj in enumerate(thisok):
data = get_lum_all(facs[jj], tags[0], bins = bins, LF = True)
hist[jj] = data/(binwidth*vol)
comm.Reduce(hist, hists, op=MPI.SUM, root=0)
if rank == 0:
np.savez('UVLF_z{}.npz'.format(z), UVLF = hists, UVMag = bincen, fac = facs)
# 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, z in enumerate(zs):
df = pd.read_csv('Magnitude_limits.txt')
low = np.array(df[filters])[ii]
bins = -np.arange(-low, 26, 0.5)[::-1]
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
parent_volume = (3200)**3
out, hist, err = get_lum_all(tags[ii], bins=bins)
Msim = out / (binwidth * vol)
xerr = np.ones(len(out)) * binwidth[0] / 2.
yerr = err / (vol * binwidth)
mask = np.where(hist == 1)[0]
uplims = np.zeros(len(bincen))
uplims[mask] = True
y_lo = np.log10(Msim) - np.log10(Msim - yerr)
y_up = np.log10(Msim + yerr) - np.log10(Msim)
y_lo[mask] = 4.
observed = Msim * (binwidth * parent_volume)
sigma = observed / np.sqrt(hist)
yy = fit_function(model, observed, sigma, bins, z)
ext_curves = ['Default', 'Calzetti1.0', 'SMC1.0', 'N181.0']
labels = ['Default', 'Calzetti', 'SMC', 'N18']
colors = ['black', 'brown', 'grey', 'orange']
ws = np.array([])
for jj in range(40):
ws = np.append(ws, np.ones(np.shape(mstar[jj]))*weights[jj])
mstar = np.concatenate(mstar)
for ii, curves in enumerate(ext_curves):
if ii==0:
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))
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])
else:
OIII4959_lum = np.concatenate(get_all(F'BPASS_2.2.1/Chabrier300/Lines/DustModelI_{curves}/OIII4959/Luminosity', tag))
OIII4959_EW = np.concatenate(get_all(F'BPASS_2.2.1/Chabrier300/Lines/DustModelI_{curves}/OIII4959/EW', tag))
OIII5007_lum = np.concatenate(get_all(F'BPASS_2.2.1/Chabrier300/Lines/DustModelI_{curves}/OIII5007/Luminosity', tag))
OIII5007_EW = np.concatenate(get_all(F'BPASS_2.2.1/Chabrier300/Lines/DustModelI_{curves}/OIII5007/EW', tag))
# y = get_hist(0, tags_ref[0], bins = bins, inp = 'REF', LF = True)/(rho_crit_ref * refvol)
# axs.plot(bincen, np.log10(y), lw = 2, ls = 'dashed', color='red', label = 'EAGLE Ref')
#
# y = get_hist(0, tags_ref[0], bins = bins, inp = 'AGNdT9', LF = True)/(rho_crit_ref * AGNdT9vol)
# axs.plot(bincen, np.log10(y), lw = 2, ls = 'dotted', color='red', label = 'EAGLE AGNdT9')
for ii, jj in enumerate(tags):
df = pd.read_csv('Magnitude_limits.txt')
low = np.array(df[filter])[ii]
bins = -np.arange(-low, 25, 0.5)[::-1]
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
if input == plt_options[1]:
LFUV = get_lum_all(jj, LF=False, filter='FUV', Luminosity='DustModelI')
LFUV_int = get_lum_all(jj,
LF=False,
filter='FUV',
Luminosity='Intrinsic')
xlabel = r'M$_{1500}$' #\mathrm{(Intrinsic)}$'
ylabel = r'A$_{\mathrm{FUV}}$=-2.5 log$_{10}$(L$_{\mathrm{FUV}}^{\mathrm{Observed}}$/L$_{\mathrm{FUV}}^{\mathrm{Intrinsic}}$)'
ylim = (0., 3.7)
savename = 'att_env_obs.pdf'
axs[ii].text(-21.3, 3.2, r'$z = {}$'.format(zs[ii]), fontsize=13)
if input == plt_options[2]:
LFUV = get_lum_all(jj, LF=False, filter='FUV', Luminosity='DustModelI')
LNUV = get_lum_all(jj, LF=False, filter='NUV', Luminosity='DustModelI')
xlabel = r'M$_{1500}$' #\mathrm{(Intrinsic)}$'
ylabel = r'$\beta$'