def get_lum(sim, kappa, tag, BC_fac, IMF='Chabrier_300',
bins=np.arange(-24, -16, 0.5), inp='FLARES', LF=True,
filters=('FAKE.TH.FUV',), Type='Total', log10t_BC=7.,
extinction='default', orientation="sim", masslim=None, nlim=None):
Lums = lum(sim, kappa, tag, BC_fac=BC_fac, IMF=IMF, inp=inp, LF=LF,
filters=filters, Type=Type, log10t_BC=log10t_BC,
extinction=extinction, orientation=orientation,
masslim=masslim, nlim=nlim)
# try:
# Lums = lum(sim, kappa, tag, BC_fac=BC_fac, IMF=IMF, inp=inp, LF=LF,
# filters=filters, Type=Type, log10t_BC=log10t_BC,
# extinction=extinction, orientation=orientation,
# masslim=masslim)
#
# except Exception as e:
# Lums = {f: np.array([], dtype=np.float64) for f in filters}
# Lums["coords"] = np.array([], dtype=np.float64)
# Lums["smls"] = np.array([], dtype=np.float64)
# Lums["masses"] = np.array([], dtype=np.float64)
# Lums["nstar"] = np.array([], dtype=np.float64)
# Lums["begin"] = np.array([], dtype=np.float64)
# Lums["end"] = np.array([], dtype=np.float64)
# print(e)
if LF:
tmp, edges = np.histogram(lum_to_M(Lums), bins=bins)
return tmp
else:
return Lums
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_lum(sim,
kappa,
tag,
BC_fac,
IMF='Chabrier_300',
bins=np.arange(-24, -16, 0.5),
inp='FLARES',
LF=True,
filters=['FAKE.TH.FUV'],
Type='Total',
log10t_BC=7.,
extinction='default',
data_folder='data',
aperture='30'):
try:
Lums = lum(sim,
kappa,
tag,
BC_fac=BC_fac,
IMF=IMF,
inp=inp,
LF=LF,
filters=filters,
Type=Type,
log10t_BC=log10t_BC,
extinction=extinction,
data_folder=data_folder,
aperture=aperture)
except Exception as e:
Lums = np.ones(len(filters)) * np.nan
print(e)
if LF:
tmp, edges = np.histogram(lum_to_M(Lums), bins=bins)
return tmp
else:
return Lums
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
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
