def lf_for_z(z, log10L_bin_centres, models, legend=False):
# THIS WILL BE individual LF plot
colors = [
'b', 'r', 'orange', 'g', 'k', 'c', 'y', 'm', 'darkslateblue', 'gray',
'tomato', 'lawngreen', 'teal', 'wheat'
]
fig = plt.figure()
if type(models) == str:
models = [models]
for i, model in enumerate(models):
m = getattr(lf_parameters, model)()
if hasattr(m, 'beta'):
s = abs(np.array(m.redshifts) - z) < 0.1
x = 0.4 * (lum_to_M(10**log10L_bin_centres) -
np.array(m.M_star)[s][0])
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
beta = np.array(m.beta)[s][0]
phi = phistar / (10**(x * (alpha + 1)) + 10**(x * (beta + 1)))
plt.scatter(log10L_bin_centres,
np.log10(phi),
c=colors[i],
label=model)
else:
s = abs(np.array(m.redshifts) - z) < 0.1
x = 10**(log10L_bin_centres -
np.log10(M_to_lum(np.array(m.M_star)[s][0])))
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
plt.scatter(log10L_bin_centres,
np.log10(phistar * (x)**(alpha + 1) * np.exp(-x)),
c=colors[i],
label=model)
plt.ylabel(r'$\rm log_{10}(\phi \; / \; cMpc^{-3} \; dex^{-1})$')
plt.xlabel(r"$\rm log_{10}(L_{UV} \; / \; erg\, s^{-1}\, Hz^{-1})$")
if legend:
plt.legend(loc='best')
return fig
def get_data(ii, tag, inp = 'FLARES'):
num = str(ii)
if inp == 'FLARES':
if len(num) == 1:
num = '0'+num
sim = rF"../flares_pipeline/data/FLARES_{num}_sp_info.hdf5"
num = ''#num+'/'
else:
sim = rF"../flares_pipeline/data/EAGLE_{inp}_sp_info.hdf5"
num=''
with h5py.File(sim, 'r') as hf:
cop = np.array(hf[num+tag+'/Galaxy'].get('COP'), dtype = np.float64)
mstar = np.array(hf[num+tag+'/Galaxy'].get('Mstar_30'), dtype = np.float64)*1e10
S_len = np.array(hf[num+tag+'/Galaxy'].get('S_Length'), dtype = np.int64)
G_len = np.array(hf[num+tag+'/Galaxy'].get('G_Length'), dtype = np.int64)
S_coords = np.array(hf[num+tag+'/Particle'].get('S_Coordinates'), dtype = np.float64)
G_coords = np.array(hf[num+tag+'/Particle'].get('G_Coordinates'), dtype = np.float64)
S_mass = np.array(hf[num+tag+'/Particle'].get('S_MassInitial'), dtype = np.float64)*1e10
G_mass = np.array(hf[num+tag+'/Particle'].get('G_Mass'), dtype = np.float64)*1e10
S_Z = np.array(hf[num+tag+'/Particle'].get('S_Z_smooth'), dtype = np.float64)
S_age = np.array(hf[num+tag+'/Particle'].get('S_Age'), dtype = np.float64)*1e3
S_los = np.array(hf[num+tag+'/Particle'].get('S_los'), dtype = np.float64)
S_sml = np.array(hf[num+tag+'/Particle'].get('S_sml'), dtype = np.float64)
G_sml = np.array(hf[num+tag+'/Particle'].get('G_sml'), dtype = np.float64)
G_Z = np.array(hf[num+tag+'/Particle'].get('G_Z_smooth'), dtype = np.float64)
lfuv = lum_to_M(np.array(hf[num+tag+'/Galaxy/BPASS_2.2.1/Chabrier300/Luminosity/DustModelI'].get('FUV'), dtype = np.float64))
begin = np.zeros(len(S_len), dtype = np.int64)
end = np.zeros(len(S_len), dtype = np.int64)
begin[1:] = np.cumsum(S_len)[:-1]
end = np.cumsum(S_len)
gbegin = np.zeros(len(G_len), dtype = np.int64)
gend = np.zeros(len(G_len), dtype = np.int64)
gbegin[1:] = np.cumsum(G_len)[:-1]
gend = np.cumsum(G_len)
return S_len, cop, mstar, S_coords, G_coords, S_mass, G_mass, S_Z, S_age, S_los, S_sml, G_sml, G_Z, begin, end, gbegin, gend, S_len, lfuv
def get_hist(ii, tag, bins, inp='GEAGLE', filter='FUV', Luminosity='Dustcorr'):
if inp == 'GEAGLE':
num = str(ii)
if len(num) == 1:
num = '0' + num
filename = '../photometry/out/GEAGLE_{}_sp_info.hdf5'.format(num)
with h5py.File(filename, 'r') as hf:
lum = np.array(hf[
F"{tag}/Subhalo/BPASS/SalpeterIMF/ModelI/Luminosity/{Luminosity}/{filter}"]
)
tmp, edges = np.histogram(lum_to_M(lum), bins=bins)
return tmp
def get_line(ii,
tag,
line='HI6563',
inp='FLARES',
LF=False,
bins=np.arange(42, 46, 0.5),
Type='DustModelI'):
if inp == 'FLARES':
num = str(ii)
if len(num) == 1:
num = '0' + num
filename = './data/flares.hdf5'
num = num + '/'
else:
filename = F'./data/EAGLE_{inp}_sp_info.hdf5'
num = ''
with h5py.File(filename, 'r') as hf:
if LF:
lum = np.array(hf[
F"{num}{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{Type}/{line}/Luminosity"]
)
tmp, edges = np.histogram(lum_to_M(lum), bins=bins)
return tmp
else:
lum = np.array(hf[
F"{num}{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{Type}/{line}/Luminosity"]
)
EW = np.array(hf[
F"{num}{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Lines/{Type}/{line}/EW"]
)
return lum, EW
def get_lum(ii,
tag,
bins=np.arange(-26, -16, 0.5),
inp='FLARES',
filter='FUV',
LF=True,
Luminosity='DustModelI'):
if inp == 'FLARES':
num = str(ii)
if len(num) == 1:
num = '0' + num
filename = rF"./data/flares.hdf5"
num = num + '/'
else:
filename = F'./data/EAGLE_{inp}_sp_info.hdf5'
num = ''
with h5py.File(filename, 'r') as hf:
lum = np.array(hf[
F"{num}/{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Luminosity/{Luminosity}/{filter}"]
)
if LF == True:
tmp, edges = np.histogram(lum_to_M(lum), bins=bins)
return tmp
else:
return lum
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))
ws = np.array([])
for jj in sims:
ws = np.append(ws, np.ones(np.shape(x_inp[jj]))*weights[jj])
x_inp = np.concatenate(x_inp)
create_EW_plot(x_inp, np.log10(Halpha), bins, ws, axs[0], titles[0], s_m.to_rgba(ii+0.5), ii)
create_EW_plot(x_inp, np.log10(Hbeta), bins, ws, axs[1], titles[1], s_m.to_rgba(ii+0.5), ii)
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
beta = np.log10(L_FUV / L_NUV) / np.log10(1500. / 2500.) - 2.0
att = -2.5 * np.log10(L_FUV / L_FUV_int)
zpos = -17.5
if tag == '010_z005p000':
from astropy.cosmology import Planck13
from astropy import units as u
tmp = 1 / Planck13.H(5).decompose()
print((1.0 / (3 * tmp.to(u.yr))).value)
check = np.logical_and(Mstar_30 > 1e10, att < 0.4)
print(sfr_30[check])
def m_to_M(m, cosmo, z):
flux = photconv.m_to_flux(m)
lum = photconv.flux_to_L(flux, cosmo, z)
M = photconv.lum_to_M(lum)
return M
def lf_multi(zs, log10L_bin_centres, models, binned_lf=False, legend=False):
# THIS WILL BE LF PLOT FOR A SELECTION OF MODELS
markers = ['D', 's', 'h', '^', 'o', '*', 'v']
cmap = mpl.cm.tab20
cmap_marker = mpl.cm.Dark2_r
if len(zs) % 2 == 1:
N = int(len(zs) / 2) + 1
else:
N = int(len(zs) / 2)
fig, axes = plt.subplots(N,
2,
figsize=(6, N * 2),
sharex=True,
sharey=True)
plt.subplots_adjust(left=0.10,
bottom=0.10,
top=0.95,
right=0.85,
wspace=0.,
hspace=-0.)
if type(models) == str:
models = [models]
Nxx = len(models)
for z, ax in zip(zs, axes.flatten()):
if legend:
if Nxx > 7:
if ax == axes[0, 0]:
try:
g = []
for i, model in enumerate(models[:7]):
label = getattr(lf_parameters, model)().label
g.append(
Line2D([-99., -98.], [-99., -98.],
color=cmap(i / 19),
label=label,
alpha=0.6))
ax.legend(handles=g, loc='lower left', fontsize=6)
except:
continue
if ax == axes[0, 1]:
try:
g = []
for i, model in enumerate(models[7:]):
i = i + 7
label = getattr(lf_parameters, model)().label
g.append(
Line2D([-99., -98.], [-99., -98.],
color=cmap(i / 19),
label=label,
alpha=0.6))
ax.legend(handles=g, loc='lower left', fontsize=6)
except:
continue
else:
if ax == axes[0, 0]:
try:
g = []
for i, model in enumerate(models):
label = getattr(lf_parameters, model)().label
g.append(
Line2D([-99., -98.], [-99., -98.],
color=cmap(i / 19),
label=label,
alpha=0.6))
ax.legend(handles=g, loc='lower left', fontsize=6)
except:
continue
if ax == axes[1, 0]:
if binned_lf:
if type(binned_lf) == list:
g = []
for l, lf in enumerate(binned_lf):
g.append(
plt.errorbar(-99.,
-99.,
yerr=1.,
color=cmap_marker(l / 7),
linestyle='',
marker=markers[l],
mec='k',
alpha=0.6,
label=lf['label'],
markersize=3,
elinewidth=1,
capsize=1,
capthick=1))
ax.legend(handles=g, loc='lower left', fontsize=6)
else:
g = []
for l in range(1):
g.append(
plt.errorbar(-99.,
-99.,
yerr=1.,
fmt='kD',
alpha=0.6,
label=binned_lf['label'],
markersize=3,
elinewidth=1,
capsize=1,
capthick=1))
ax.legend(handles=g, loc='lower left', fontsize=6)
# except:
# continue
for i, model in enumerate(models):
m = getattr(lf_parameters, model)()
if hasattr(m, 'beta'):
s = abs(np.array(m.redshifts) - z) < 0.5
try:
x = 0.4 * (lum_to_M(10**log10L_bin_centres) -
np.array(m.M_star)[s][0])
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
beta = np.array(m.beta)[s][0]
phi = phistar / (10**(x * (alpha + 1)) + 10**(x *
(beta + 1)))
ax.plot(log10L_bin_centres,
np.log10(phi),
c=cmap(i / 19),
alpha=0.6)
except:
continue
else:
s = abs(np.array(m.redshifts) - z) < 0.5
try:
x = 10**(log10L_bin_centres -
np.log10(M_to_lum(np.array(m.M_star)[s][0])))
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
ax.plot(log10L_bin_centres,
np.log10(phistar * (x)**(alpha + 1) * np.exp(-x)),
c=cmap(i / 19),
alpha=0.6)
except:
continue
if binned_lf:
if type(binned_lf) == list:
for l, lf in enumerate(binned_lf):
try:
q = abs(
np.array([float(k)
for k in [*lf['LF'].keys()]]) - z) < 0.5
z_key = str(
int(
np.array([
float(k) for k in [*lf['LF'].keys()]
])[q]))
log10L_bin = np.log10(
M_to_lum(np.array(lf['LF'][str(int(z_key))]['M'])))
phi_bin = np.array(lf['LF'][str(int(z_key))]['phi'])
err = np.array(lf['LF'][str(int(z_key))]['phi_err'])
uplims = lf['LF'][str(int(z_key))]['uplim']
if lf['both_err']:
if lf['log_err']:
phi_err = err
else:
phi_err = [
logerr_lo(phi_bin, err[0], uplims),
logerr_hi(phi_bin, err[1])
]
else:
if lf['log_err']:
phi_err = err
else:
phi_err = [
logerr_lo(phi_bin, err, uplims),
logerr_hi(phi_bin, err)
]
ax.errorbar(log10L_bin,
np.log10(phi_bin),
yerr=phi_err,
color=cmap_marker(l / 7),
linestyle='',
marker=markers[l],
mec='k',
alpha=0.8,
zorder=50 + l,
markersize=3,
elinewidth=1,
capsize=1,
capthick=1,
uplims=uplims)
except:
continue
else:
try:
q = abs(
np.array([float(k)
for k in [*binned_lf['LF'].keys()]]) -
z) < 0.5
z_key = str(
int(
np.array([
float(k) for k in [*binned_lf['LF'].keys()]
])[q]))
log10L_bin = np.log10(
M_to_lum(
np.array(binned_lf['LF'][str(int(z_key))]['M'])))
phi_bin = np.array(binned_lf['LF'][str(int(z_key))]['phi'])
err = np.array(binned_lf['LF'][str(int(z_key))]['phi_err'])
uplims = binned_lf['LF'][str(int(z_key))]['uplim']
if binned_lf['both_err']:
if binned_lf['log_err']:
phi_err = err
else:
phi_err = [
logerr_lo(phi_bin, err[0], uplims),
logerr_hi(phi_bin, err[1])
]
print(phi_err)
else:
if binned_lf['log_err']:
phi_err = err
else:
phi_err = [
logerr_lo(phi_bin, err, uplims),
logerr_hi(phi_bin, err)
]
ax.errorbar(log10L_bin,
np.log10(phi_bin),
yerr=phi_err,
fmt='kD',
alpha=0.6,
zorder=50,
markersize=3,
elinewidth=1,
capsize=1,
capthick=1,
uplims=uplims)
except:
continue
ax.text(0.7,
0.9,
r'$\rm z=[{0:.1f}, {1:.1f})$'.format(z - 0.5, z + 0.5),
fontsize=8,
transform=ax.transAxes)
ylim = np.array([-8., -4.01])
ax.set_ylim(-8., -1.5)
ax.set_xlim([min(log10L_bin_centres), max(log10L_bin_centres)])
fig.text(0.5,
0.05,
r'$\rm\log_{10}[L_{FUV} \; / \; (erg \; s^{-1} \; Hz^{-1})]$',
horizontalalignment='center',
verticalalignment='center')
fig.text(0.05,
0.5,
r'$\rm\log_{10}[\phi \; / \; Mpc^{-3}]$',
horizontalalignment='center',
verticalalignment='center',
rotation=90)
return fig
def lf_params_zs(zs, log10L_bin_centres, models, legend=False):
# THIS WILL BE LF PLOT FOR A SELECTION OF MODELS
colours = [
'b', 'r', 'orange', 'g', 'k', 'c', 'y', 'm', 'darkslateblue', 'gray',
'tomato', 'lawngreen', 'teal', 'wheat'
]
if len(zs) % 2 == 1:
N = int(len(zs) / 2) + 1
else:
N = int(len(zs) / 2)
fig, axes = plt.subplots(N,
2,
figsize=(6, N * 2),
sharex=True,
sharey=True)
plt.subplots_adjust(left=0.10,
bottom=0.10,
top=0.95,
right=0.85,
wspace=0.,
hspace=-0.)
if type(models) == str:
models = [models]
Nxx = len(models)
for z, ax in zip(zs, axes.flatten()):
if legend:
if Nxx > 7:
if ax == axes[0, 0]:
try:
g = []
for i, model in enumerate(models[:7]):
colors_plot = colours[:7]
g.append(
ax.scatter([-99.], [-99.],
c=colors_plot[i],
s=5,
label=model))
ax.legend(handles=g, loc='lower left')
except:
continue
if ax == axes[0, 1]:
try:
g = []
for i, model in enumerate(models[7:]):
colors_plot = colours[7:]
g.append(
ax.scatter([-99.], [-99.],
c=colors_plot[i],
s=5,
label=model))
ax.legend(handles=g, loc='lower left')
except:
continue
else:
if ax == axes[0, 0]:
try:
g = []
for i, model in enumerate(models):
g.append(
ax.scatter([-99.], [-99.],
c=colours[i],
s=5,
label=model))
ax.legend(handles=g, loc='lower left')
except:
continue
for i, model in enumerate(models):
m = getattr(lf_parameters, model)()
if hasattr(m, 'beta'):
s = abs(np.array(m.redshifts) - z) < 0.5
try:
x = 0.4 * (lum_to_M(10**log10L_bin_centres) -
np.array(m.M_star)[s][0])
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
beta = np.array(m.beta)[s][0]
phi = phistar / (10**(x * (alpha + 1)) + 10**(x *
(beta + 1)))
ax.scatter(log10L_bin_centres,
np.log10(phi),
s=5,
c=colours[i])
except:
continue
else:
s = abs(np.array(m.redshifts) - z) < 0.5
try:
x = 10**(log10L_bin_centres -
np.log10(M_to_lum(np.array(m.M_star)[s][0])))
phistar = 10**np.array(m.phi_star)[s][0]
alpha = np.array(m.alpha)[s][0]
ax.scatter(log10L_bin_centres,
np.log10(phistar * (x)**(alpha + 1) *
np.exp(-x)),
s=5,
c=colours[i])
except:
continue
ax.text(0.7,
0.9,
r'$\rm z=[{0:.1f}, {1:.1f})$'.format(z - 0.5, z + 0.5),
fontsize=8,
transform=ax.transAxes)
ylim = np.array([-8., -4.01])
ax.set_ylim(-8., -1.5)
ax.set_xlim([min(log10L_bin_centres), max(log10L_bin_centres)])
fig.text(0.5,
0.05,
r'$\rm\log_{10}[L_{FUV} \; / \; (erg \; s^{-1} \; Hz^{-1})]$',
horizontalalignment='center',
verticalalignment='center')
fig.text(0.05,
0.5,
r'$\rm\log_{10}[\phi \; / \; Mpc^{-3}]$',
horizontalalignment='center',
verticalalignment='center',
rotation=90)
return fig
bincen = (bins[1:]+bins[:-1])/2.
binwidth = bins[1:] - bins[:-1]
L = {}
for f in ['FUV','NUV']:
L[f] = np.array([])
w = np.array([])
with h5py.File(f'./data1/flares.hdf5', 'r') as hf:
for sim in hf.keys():
tmp = np.array(hf[f'{sim}/{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Luminosity/DustModelI_{kappa_BC}/{f}'])
L[f] = np.hstack((L[f], tmp))
w = np.append(w, np.ones(len(tmp))*weights[int(sim)])
beta = np.log10(L['FUV']/L['NUV'])/np.log10(1500./2500.) - 2.0
x, y = lum_to_M(L['FUV']), beta
out = fl.binned_weighted_quantile(x, y, w, bins, quantiles)
hist, binedges = np.histogram(x, bins)
ok = np.where(hist>0)[0]
ok1 = np.where(hist[ok]>3)[0][0]
axs.fill_between(bincen[ok][ok1:], out[:,0][ok][ok1:], out[:,2][ok][ok1:], color=s_m.to_rgba(kappa_BC), alpha=0.25)
axs.plot(bincen[ok], out[:,1][ok], ls='dashed', color=s_m.to_rgba(kappa_BC), alpha=1, lw=2)
axs.plot(bincen[ok][ok1:], out[:,1][ok][ok1:], ls='-', color=s_m.to_rgba(kappa_BC), alpha=1, lw=2, label=r"$\kappa_{\mathrm{BC}}=%s$"%(str(kappa_BC)))
plot_beta(5, axs)
axs.set_ylim(ylims)
axs.set_xlim(xlims)
axs.minorticks_on()
axs.tick_params(axis='x', which='minor', direction='in')
L[f] = np.array([])
mstar, sfr = np.array([]), np.array([])
w = np.array([])
with h5py.File(f'data/flares.hdf5', 'r') as hf:
for sim in hf.keys():
tmp = np.array(hf[
f'{sim}/{tag}/Galaxy/BPASS_2.2.1/Chabrier300/Luminosity/DustModelI/{f}']
)
L[f] = np.hstack((L[f], tmp))
w = np.append(w, np.ones(len(tmp)) * weights[int(sim)])
beta = np.log10(L['FUV'] / L['NUV']) / np.log10(1500. / 2500.) - 2.0
y = beta
if input == plt_options[0]:
x = lum_to_M(L['FUV'])
else:
x = np.log10(get_data_all(tag, inp='FLARES', DF=False) * 1e10)
axs[ii].hexbin(x,
y,
gridsize=(35, 13),
bins='log',
cmap='Greys_r',
linewidths=0.,
mincnt=5,
extent=[*[low, -24.5], *ylims],
alpha=0.6,
zorder=2)
quantiles = [0.84, 0.50, 0.16]
def N(self,
area=1.,
cosmo=False,
redshift_limits=[8., 15.],
log10L_limits=[27.5, 30.],
dz=0.05,
dlog10L=0.05,
per_arcmin=False,
return_volumes=False):
# calculates the number of galaxies in each bin on a grid defined by redshift_limits, log10L_limits, dz, dlog10L
# and area based on a luminosity function evolution model.
area_sm = area # Area in square arcmin
area_sd = area_sm / 3600. # Area in square degrees
area_sr = (np.pi / 180.)**2 * area_sd # Area in steradian
if not cosmo: cosmo = FLARE.core.default_cosmo()
# Setting the bin edges as well as centres for later operations
bin_edges = {
'log10L':
np.arange(log10L_limits[0], log10L_limits[-1] + dlog10L, dlog10L),
'z':
np.arange(redshift_limits[0], redshift_limits[-1] + dz, dz)
}
bin_centres = {
'log10L': bin_edges['log10L'][:-1] + dlog10L / 2.,
'z': bin_edges['z'][:-1] + dz / 2.
}
# Using astropy.cosmology to calculate the volume in each redshift bin
volumes = np.asarray([
cp.quad(dVc, bin_edges['z'][i - 1], bin_edges['z'][i],
args=cosmo)[0] for i in range(1, len(bin_edges['z']))
])
params = self.parameters(bin_centres['z'])
if 'beta' in params.keys():
alphas = params['alpha']
Lstars = M_to_lum(params['M*'])
Mstars = params['M*']
phistars = 10**params['log10phi*']
betas = params['beta']
N = phistars[None, :] * np.vectorize(quadfunct2)(
_integ_dblpow, 0.4 *
(lum_to_M(10**bin_edges['log10L'][1:, None]) -
Mstars[None, :]), 0.4 *
(lum_to_M(10**bin_edges['log10L'][:-1, None]) -
Mstars[None, :]), alphas[None, :],
betas[None, :]) * volumes[None, :] * area_sr
''' Left in for possible future implementation
#N = phistars[None, :] * np.vectorize(quadfunct2)(_integ_dblpow2,
# 10**(bin_edges['log10L'][:-1, None] - Lstars[None, :]),
# 10**(bin_edges['log10L'][1:, None] - Lstars[None, :]),
# alphas[None, :], betas[None, :] ) * volumes[None, :] * area_sr
'''
else:
alphas = params['alpha']
Lstars = M_to_lum(params['M*'])
phistars = 10**params['log10phi*']
Mstars = params['M*']
N = phistars[None, :] * np.vectorize(quadfunct)(
_integ,
10**bin_edges['log10L'][:-1, None] / Lstars[None, :],
10**bin_edges['log10L'][1:, None] / Lstars[None, :],
args=(alphas[None, :])) * volumes[None, :] * area_sr
''' Left in for possible future implementation
N = phistars[None, :] * np.vectorize(quadfunct)(_integ2,
lum_to_M(10**bin_edges['log10L'][1:, None]) - Mstars[None, :],
lum_to_M(10**bin_edges['log10L'][:-1, None]) - Mstars[None, :],
args=(alphas[None, :])) * volumes[None, :] * area_sr
'''
if per_arcmin:
N /= area_sm
if return_volumes:
return bin_edges, bin_centres, volumes, N
else:
return bin_edges, bin_centres, N
mstars = np.append(mstars, mstar)
lfuvs = np.append(lfuvs, lfuv)
att_gals = np.append(att_gals, att_gal)
else:
atts, mstars, lfuvs, att_gals = att, mstar, lfuv, att_gal
mstars = np.log10(mstars)
mbins = np.arange(9., 11.75, 0.5)
mbins_label = np.array(
[F'${mbins[ii]}$ - ${mbins[ii+1]}$' for ii in range(len(mbins) - 1)])
lbins = -np.arange(19.5, 25, 1)[::-1]
lbins_label = np.array(
[F'${lbins[ii+1]}$ - ${lbins[ii]}$' for ii in range(len(lbins) - 1)])
atts = -2.5 * np.log10(atts)
lfuvs = lum_to_M(lfuvs)
fig, axs = plt.subplots(nrows=1,
ncols=2,
figsize=(10, 5),
sharex=True,
sharey=True,
facecolor='w',
edgecolor='k')
axs = axs.ravel()
colors = ['red', 'brown', 'magenta', 'orange', 'indigo']
for ii in range(len(mbins) - 1):
ok = np.logical_and(mstars >= mbins[ii], mstars < mbins[ii + 1])
if np.sum(ok) >= 1:
ls='solid',
marker='o',
yerr=[
np.log10(yy[nonzero]) -
np.log10(yy[nonzero] - yyerr[nonzero]),
np.log10(yy[nonzero] + yyerr[nonzero]) -
np.log10(yy[nonzero])
],
color=s_m.to_rgba(
(dbins[kk] + dbins[kk + 1]) / 2))
elif input == plt_options[1]:
LFUV_this = np.concatenate(LFUV[ok])
LFUV_int_this = np.concatenate(LFUV_int[ok])
y = -2.5 * np.log10(LFUV_this / LFUV_int_this)
x = lum_to_M(LFUV_this)
quantiles = [0.84, 0.50, 0.16]
out = fl.binned_weighted_quantile(x, y, np.ones(len(x)), bins,
quantiles)
hist, binedges = np.histogram(x, bins)
tok = np.where(hist > 0)[0]
axs[ii].errorbar(bincen[tok],
out[:, 1][tok],
lw=2,
ls='solid',
marker='o',
yerr=[
out[:, 1][tok] - out[:, 2][tok],
out[:, 0][tok] - out[:, 1][tok]
],
slen = np.append(slen, dat[ii][-1])
num = np.append(num, num[-1]+len(dat[ii][0]))
part_num = np.append(part_num, np.arange(0, len(dat[ii][0])))
slen = np.asarray(slen, dtype=np.int)
violin_att = np.ones((len(mstar), np.max(slen)))*np.nan
for ii in range(len(mstar)):
jj = np.where(ii>=num)[0][-1]
begin = dat[jj][4][part_num[ii]]
end = dat[jj][5][part_num[ii]]
violin_att[ii][:slen[ii]] = dat[jj][3][begin:end]
att_gal = -2.5*np.log10(lfuv/lfuv_int)
lfuv = lum_to_M(lfuv)
mstars = np.log10(mstar)
mbins = np.arange(9., 11.75, 0.5)
mbins_label = np.array([F'${mbins[ii]}$ - ${mbins[ii+1]}$' for ii in range(len(mbins)-1)])
lbins = -np.arange(19, 25, 1)[::-1]
lbins_label = np.array([F'${lbins[ii]}$ - ${lbins[ii+1]}$' for ii in range(len(lbins)-1)])
if inp == 0:
fig, axs = plt.subplots(nrows = 2, ncols = 5, figsize=(11, 5), sharex=True, sharey=True, facecolor='w', edgecolor='k')
axs = axs.ravel()
for ii in range(len(mbins)-1):
ok = np.logical_and(mstars>=mbins[ii], mstars<mbins[ii+1])
weights = np.array(df['weights'])
for ii, jj in enumerate(z):
data = get_all(tags[ii], Luminosity='DustModelI', filter=filters)
for kk in range(len(weights)):
if kk == 0:
Mlum = data[kk][0]
part = data[kk][1]
slen = data[kk][2]
w = np.ones(len(data[kk][2])) * weights[kk]
else:
Mlum = np.append(Mlum, data[kk][0], axis=0)
part = np.append(part, data[kk][1])
slen = np.append(slen, data[kk][2])
w = np.append(w, np.ones(len(data[kk][2])) * weights[kk])
ok = np.where(part == 100)[0]
percentile[ii] = np.percentile(Mlum[ok], 97, axis=0)
for ii, jj in enumerate(filters):
df_filter_lims[jj[8:]] = lum_to_M(percentile[:, ii])
print(df_filter_lims)
df_filter_lims.to_csv('Magnitude_limits.txt')
tags = [
'010_z005p000', '009_z006p000', '008_z007p000', '007_z008p000',
'006_z009p000', '005_z010p000'
]
tags_ref = [
'008_z005p037', '006_z005p971', '005_z007p050', '004_z008p075',
'003_z008p988', '002_z009p993'
]
for ii in range(6):
z = ii + 5
df = pd.read_csv('Magnitude_limits.txt')
low = lum_to_M(np.array(df[filter])[ii])
bins = -np.arange(-low, 25, 0.3)[::-1]
bincen = (bins[1:] + bins[:-1]) / 2.
binwidth = bins[1:] - bins[:-1]
vol = (4 / 3) * np.pi * (14 / h)**3
out, hist = get_all(tags[ii], bins, Luminosity='Dustcorr')
phi = out / (vol * binwidth)
xerr = np.ones(len(phi)) * binwidth[0] / 2.
yerr = np.sqrt(out) / (vol * binwidth)
axs[ii].errorbar(bincen,
np.log10(phi),
yerr=[
np.log10(phi) - np.log10(phi - yerr),
np.log10(phi + yerr) - np.log10(phi)
