def kawa_fit_err(y, l, ro, b, ro_err, b_err, uplow="up"):
ro_term = ro_err * (l / M_to_lum(-21))**b
beta_term = b_err * ro * (l / M_to_lum(-21)) ** b \
* np.log(l / M_to_lum(-21))
if uplow == "up":
return y + np.sqrt(ro_term**2 + beta_term**2)
else:
return y - np.sqrt(ro_term**2 + beta_term**2)
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 sample(self, area=1., cosmo=False, redshift_limits=[8., 15.], log10L_limits=[27., 30.], dz=0.05,
seed=False):
# samples the LF evolution model in a given area
if not cosmo: cosmo = flare.core.default_cosmo()
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
# Setting the bin edges as well as centres for later operations
bin_edges = {'z': np.arange(redshift_limits[0], redshift_limits[-1] + dz, dz)}
bin_centres = {'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']))])
# Initialising the output array
sample = {'log10L': np.asarray([]), 'z': np.asarray([])}
if seed: np.random.seed(seed)
for i in range(len(bin_centres['z'])):
params = self.parameters(bin_centres['z'][i])
sp = {}
sp['alpha'] = params['alpha']
sp['phi*'] = 10 ** params['log10phi*']
sp['log10L*'] = np.log10(M_to_lum(params['M*']))
LF = LF_interpolation(sp)
samples = LF.sample(volumes[i] * area_sr, log10L_limits[0])
sample['log10L'] = np.concatenate((sample['log10L'], samples))
sample['z'] = np.concatenate(
(sample['z'], np.array([bin_centres['z'][i] + dz * (np.random.random() - 0.5) for item in samples])))
return sample
}
kawa_low_params = {
'beta': {
6: 0.09,
7: 0.09,
8: 0.78,
9: 0.27
},
'r_0': {
6: 0.15,
7: 0.15,
8: 0.26,
9: 0.74
}
}
kawa_fit = lambda l, r0, b: r0 * (l / M_to_lum(-21))**b
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 M_to_m(M, cosmo, z):
lum = photconv.M_to_lum(M)
flux = photconv.lum_to_flux(lum, cosmo, z)
m = photconv.flux_to_m(flux)
return m
# Define filter
filters = ('FAKE.TH.FUV', 'FAKE.TH.NUV', 'FAKE.TH.V')
csoft = 0.001802390 / (0.6777) * 1e3
nlim = 800
hlr_dict = {}
hlr_app_dict = {}
hlr_pix_dict = {}
lumin_dict = {}
mass_dict = {}
weight_dict = {}
lumin_bins = np.logspace(np.log10(M_to_lum(-16)), np.log10(M_to_lum(-24)), 20)
M_bins = np.linspace(-24, -16, 20)
lumin_bin_wid = lumin_bins[1] - lumin_bins[0]
M_bin_wid = M_bins[1] - M_bins[0]
lumin_bin_cents = lumin_bins[1:] - (lumin_bin_wid / 2)
M_bin_cents = M_bins[1:] - (M_bin_wid / 2)
# Load weights
df = pd.read_csv('../weight_files/weights_grid.txt')
weights = np.array(df['weights'])
regions = []
for reg in range(0, 40):
if reg < 10:
}
kawa_low_params = {
'beta': {
6: 0.09,
7: 0.09,
8: 0.78,
9: 0.27
},
'r_0': {
6: 0.15,
7: 0.15,
8: 0.26,
9: 0.74
}
}
kawa_fit = lambda l, r0, b: r0 * (l / M_to_lum(-21))**b
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 M_to_m(M, cosmo, z):
lum = photconv.M_to_lum(M)
flux = photconv.lum_to_flux(lum, cosmo, z)
m = photconv.flux_to_m(flux)
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
nlim = 10**8
hlr_dict = {}
hlr_app_dict = {}
hlr_pix_dict = {}
lumin_dict = {}
weight_dict = {}
mass_dict = {}
intr_hlr_dict = {}
intr_hlr_app_dict = {}
intr_hlr_pix_dict = {}
intr_lumin_dict = {}
intr_weight_dict = {}
lumin_bins = np.logspace(np.log10(M_to_lum(-16)), np.log10(M_to_lum(-24)), 20)
M_bins = np.linspace(-24, -16, 20)
lumin_bin_wid = lumin_bins[1] - lumin_bins[0]
M_bin_wid = M_bins[1] - M_bins[0]
lumin_bin_cents = lumin_bins[1:] - (lumin_bin_wid / 2)
M_bin_cents = M_bins[1:] - (M_bin_wid / 2)
# Load weights
df = pd.read_csv('../weight_files/weights_grid.txt')
weights = np.array(df['weights'])
regions = []
for reg in range(0, 40):
if reg < 10:
def size_lumin_grid(data, snaps, filters, orientation, Type, extinction, mtype,
weight_norm, xlims, ylims):
extent = (np.log10(xlims[0]), np.log10(xlims[1]), np.log10(ylims[0]),
np.log10(ylims[1]))
for f in filters:
print("Plotting for:")
print("Orientation =", orientation)
print("Type =", Type)
print("Filter =", f)
fig = plt.figure(figsize=(18, 5))
gs = gridspec.GridSpec(1, len(snaps))
gs.update(wspace=0.0, hspace=0.0)
axes = []
i = 0
while i < len(snaps):
axes.append(fig.add_subplot(gs[0, i]))
if i > 0:
axes[-1].tick_params(axis='y',
left=False,
right=False,
labelleft=False,
labelright=False)
i += 1
legend_elements = []
for i, snap in enumerate(snaps):
z_str = snap.split('z')[1].split('p')
z = float(z_str[0] + '.' + z_str[1])
if mtype == "part":
hlrs = np.array(data[snap][f]["HLR_0.5"])
lumins = np.array(data[snap][f]["Luminosity"])
intr_lumins = np.array(data[snap][f]["Luminosity"])
elif mtype == "app":
hlrs = np.array(data[snap][f]["HLR_Aperture_0.5"])
lumins = np.array(data[snap][f]["Image_Luminosity"])
intr_lumins = np.array(data[snap][f]["Image_Luminosity"])
else:
hlrs = np.array(data[snap][f]["HLR_Pixel_0.5"])
lumins = np.array(data[snap][f]["Image_Luminosity"])
intr_lumins = np.array(data[snap][f]["Image_Luminosity"])
w = np.array(data[snap][f]["Weight"])
mass = np.array(data[snap][f]["Mass"])
compact_ncom = data[snap][f]["Compact_Population_NotComplete"]
diffuse_ncom = data[snap][f]["Diffuse_Population_NotComplete"]
compact_com = data[snap][f]["Compact_Population_Complete"]
diffuse_com = data[snap][f]["Diffuse_Population_Complete"]
# bins = np.logspace(np.log10(0.08), np.log10(20), 40)
# lumin_bins = np.logspace(np.log10(10 ** 27.),
# np.log10(10 ** 30.5),
# 40)
# H, xbins, ybins = np.histogram2d(lumins[okinds2], hlrs[okinds2],
# bins=(lumin_bins, bins),
# weights=w[okinds2])
#
# # Resample your data grid by a factor of 3 using cubic spline interpolation.
# H = scipy.ndimage.zoom(H, 3)
#
# # percentiles = [np.min(w),
# # 10**-3,
# # 10**-1,
# # 1, 2, 5]
#
# try:
# percentiles = [np.percentile(H[H > 0], 50),
# np.percentile(H[H > 0], 80),
# np.percentile(H[H > 0], 90),
# np.percentile(H[H > 0], 95),
# np.percentile(H[H > 0], 99)]
# except IndexError:
# continue
#
# print(percentiles)
#
# bins = np.logspace(np.log10(0.08), np.log10(20), H.shape[0] + 1)
# lumin_bins = np.logspace(np.log10(10 ** 27.), np.log10(10 ** 30.5),
# H.shape[0] + 1)
#
# xbin_cents = (bins[1:] + bins[:-1]) / 2
# ybin_cents = (bins[1:] + bins[:-1]) / 2
#
# XX, YY = np.meshgrid(xbin_cents, ybin_cents)
try:
cbar = axes[i].hexbin(lumins[diffuse_ncom],
hlrs[diffuse_ncom],
gridsize=50,
mincnt=1,
C=w[diffuse_ncom],
reduce_C_function=np.sum,
xscale='log',
yscale='log',
norm=weight_norm,
linewidths=0.2,
cmap='Greys',
alpha=0.2,
extent=extent)
except ValueError as e:
print(e, "Diffuse incomplete", snap, f)
print(lumins[diffuse_ncom][lumins[diffuse_ncom] <= 0],
lumins[diffuse_ncom][lumins[diffuse_ncom] <= 0].size)
try:
axes[i].hexbin(lumins[compact_ncom],
hlrs[compact_ncom],
gridsize=50,
mincnt=1,
C=w[compact_ncom],
reduce_C_function=np.sum,
xscale='log',
yscale='log',
norm=weight_norm,
linewidths=0.2,
cmap='viridis',
alpha=0.2,
extent=extent)
except ValueError as e:
print(e, "Compact incomplete", snap, f)
print(lumins[compact_ncom][lumins[compact_ncom] <= 0],
lumins[compact_ncom][lumins[compact_ncom] <= 0].size)
try:
cbar = axes[i].hexbin(lumins[diffuse_com],
hlrs[diffuse_com],
gridsize=50,
mincnt=1,
C=w[diffuse_com],
reduce_C_function=np.sum,
xscale='log',
yscale='log',
norm=weight_norm,
linewidths=0.2,
cmap='Greys',
extent=extent)
except ValueError as e:
print(e, "Diffuse complete", snap, f)
try:
axes[i].hexbin(lumins[compact_com],
hlrs[compact_com],
gridsize=50,
mincnt=1,
C=w[compact_com],
reduce_C_function=np.sum,
xscale='log',
yscale='log',
norm=weight_norm,
linewidths=0.2,
cmap='viridis',
extent=extent)
except ValueError as e:
print(e, "Compact complete", snap, f)
# popt, pcov = curve_fit(kawa_fit, lumins, hlrs,
# p0=(kawa_params['r_0'][7],
# kawa_params['beta'][7]),
# sigma=w)
#
# popt1, pcov1 = curve_fit(kawa_fit, lumins[okinds1],
# hlrs[okinds1],
# p0=(kawa_params['r_0'][7],
# kawa_params['beta'][7]),
# sigma=w[okinds1])
#
# popt2, pcov2 = curve_fit(kawa_fit, lumins[okinds2],
# hlrs[okinds2],
# p0=(kawa_params['r_0'][7],
# kawa_params['beta'][7]),
# sigma=w[okinds2])
#
# print("Total")
# print(popt)
# print(pcov)
# print("Low")
# print(popt2)
# print(pcov2)
# print("High")
# print(popt1)
# print(pcov1)
#
# fit_lumins = np.logspace(np.log10(lumins.min()),
# np.log10(lumins.max()),
# 1000)
# fit_lumins_low = np.logspace(np.log10(lumins[okinds2].min()),
# np.log10(lumins[okinds2].max()),
# 1000)
# fit_lumins_high = np.logspace(np.log10(lumins[okinds1].min()),
# np.log10(lumins[okinds1].max()),
# 1000)
# fit = kawa_fit(fit_lumins, popt[0], popt[1])
# fit_low = kawa_fit(fit_lumins_low, popt2[0], popt2[1])
# fit_high = kawa_fit(fit_lumins_high, popt1[0], popt1[1])
#
# axes[i].plot(fit_lumins_high, fit_high,
# linestyle='-', color="m",
# alpha=0.9, zorder=5,
# linewidth=2)
# axes[i].plot(fit_lumins_low, fit_low,
# linestyle='--', color="m",
# alpha=0.9, zorder=4,
# linewidth=2)
# axes[i].plot(fit_lumins, fit,
# linestyle='dotted', color="m",
# alpha=0.9, zorder=3,
# linewidth=2)
if Type != "Intrinsic":
for p in colors_in_order:
okinds = papers == p
plt_m = mags[okinds]
plt_r_es = r_es[okinds]
plt_zs = zs[okinds]
okinds = np.logical_and(
plt_zs >= (z - 0.5),
np.logical_and(
plt_zs < (z + 0.5),
np.logical_and(plt_r_es > 0.08,
plt_m <= M_to_m(-16, cosmo, z))))
plt_m = plt_m[okinds]
plt_r_es = plt_r_es[okinds]
if plt_m.size == 0:
continue
plt_M = m_to_M(plt_m, cosmo, z)
plt_lumins = M_to_lum(plt_M)
legend_elements.append(
Line2D([0], [0],
marker=markers[p],
color='w',
label=labels[p],
markerfacecolor=colors[p],
markersize=8,
alpha=0.9))
axes[i].scatter(plt_lumins,
plt_r_es,
marker=markers[p],
label=labels[p],
s=20,
color=colors[p],
alpha=0.9)
# if int(z) in [6, 7, 8, 9]:
#
# if z == 7 or z == 6:
# low_lim = -16
# elif z == 8:
# low_lim = -16.8
# else:
# low_lim = -15.4
# fit_lumins = np.logspace(np.log10(M_to_lum(-21.6)),
# np.log10(M_to_lum(low_lim)),
# 1000)
#
# fit = kawa_fit(fit_lumins, kawa_params['r_0'][int(z)],
# kawa_params['beta'][int(z)])
# axes[i].plot(fit_lumins, fit,
# linestyle='dashed', color="g",
# alpha=0.9, zorder=2,
# label="Kawamata+18", linewidth=4)
axes[i].text(0.95,
0.05,
f'$z={z}$',
bbox=dict(boxstyle="round,pad=0.3",
fc='w',
ec="k",
lw=1,
alpha=0.8),
transform=axes[i].transAxes,
horizontalalignment='right',
fontsize=8)
axes[i].tick_params(axis='both',
which='minor',
bottom=True,
left=True)
# Label axes
axes[i].set_xlabel(r"$L_{" + f.split(".")[-1] +
"}/$ [erg $/$ s $/$ Hz]")
axes[i].tick_params(axis='x', which='both', bottom=True)
axes[i].set_xlim(xlims[0], xlims[1])
for i in range(len(axes)):
axes[i].set_ylim(ylims[0], ylims[1])
axes[0].set_ylabel('$R_{1/2}/ [pkpc]$')
axes[0].tick_params(axis='y', which='both', left=True)
uni_legend_elements = []
uni_legend_elements.append(
Line2D([0], [0],
color="k",
linestyle="none",
marker="h",
label="FLARES"))
# uni_legend_elements.append(
# Line2D([0], [0], color="g", linestyle="--",
# label=labels["K18"]))
included = []
for l in legend_elements:
if (l.get_label(), l.get_marker()) not in included:
print((l.get_label(), l.get_marker()))
uni_legend_elements.append(l)
included.append((l.get_label(), l.get_marker()))
axes[2].legend(handles=uni_legend_elements,
loc='upper center',
bbox_to_anchor=(0.5, -0.15),
fancybox=True,
ncol=len(uni_legend_elements))
fig.savefig('plots/HalfLightRadius_' + mtype + "_" + f + '_' +
orientation + '_' + Type + "_" + extinction + ".png",
bbox_inches='tight',
dpi=300)
plt.close(fig)
def fit_size_lumin_grid_nosmooth(data, snaps, filters, orientation, Type,
extinction, mtype, sample, xlims, ylims,
weight_norm):
extent = (np.log10(xlims[0]), np.log10(xlims[1]),
np.log10(ylims[0]), np.log10(ylims[1]))
for f in filters:
print("Plotting for:")
print("Orientation =", orientation)
print("Type =", Type)
print("Filter =", f)
fig = plt.figure(figsize=(18, 5))
gs = gridspec.GridSpec(1, len(snaps))
gs.update(wspace=0.0, hspace=0.0)
axes = []
i = 0
while i < len(snaps):
axes.append(fig.add_subplot(gs[0, i]))
axes[-1].loglog()
if i > 0:
axes[-1].tick_params(axis='y', left=False, right=False,
labelleft=False, labelright=False)
i += 1
legend_elements = []
for i, snap in enumerate(snaps):
z_str = snap.split('z')[1].split('p')
z = float(z_str[0] + '.' + z_str[1])
if mtype == "part":
hlrs = np.array(data[snap][f]["HLR_0.5"])
lumins = np.array(data[snap][f]["Luminosity"])
intr_lumins = np.array(data[snap][f]["Luminosity"])
elif mtype == "app":
hlrs = np.array(data[snap][f]["HLR_Aperture_0.5"])
lumins = np.array(data[snap][f]["Image_Luminosity"])
intr_lumins = np.array(data[snap][f]["Image_Luminosity"])
else:
hlrs = np.array(data[snap][f]["HLR_Pixel_0.5_No_Smooth"])
lumins = np.array(data[snap][f]["Image_Luminosity"])
intr_lumins = np.array(data[snap][f]["Image_Luminosity_No_Smooth"])
w = np.array(data[snap][f]["Weight"])
mass = np.array(data[snap][f]["Mass"])
compact_ncom = data[snap][f]["Compact_Population_NotComplete"]
diffuse_ncom = data[snap][f]["Diffuse_Population_NotComplete"]
compact_com = data[snap][f]["Compact_Population_Complete"]
diffuse_com = data[snap][f]["Diffuse_Population_Complete"]
if sample == "Complete":
complete = np.logical_or(compact_com, diffuse_com)
else:
complete = data[snap][f]["okinds"]
try:
cbar = axes[i].hexbin(lumins[diffuse_ncom], hlrs[diffuse_ncom],
gridsize=50,
mincnt=1, C=w[diffuse_ncom],
reduce_C_function=np.sum,
xscale='log', yscale='log',
norm=weight_norm, linewidths=0.2,
cmap='Greys', alpha=0.2, extent=extent)
except ValueError as e:
print(e, "Diffuse incomplete", snap, f)
print(lumins[diffuse_ncom][lumins[diffuse_ncom] <= 0],
lumins[diffuse_ncom][lumins[diffuse_ncom] <= 0].size)
try:
axes[i].hexbin(lumins[compact_ncom], hlrs[compact_ncom],
gridsize=50,
mincnt=1,
C=w[compact_ncom],
reduce_C_function=np.sum,
xscale='log', yscale='log',
norm=weight_norm, linewidths=0.2,
cmap='plasma', alpha=0.2, extent=extent)
except ValueError as e:
print(e, "Compact incomplete", snap, f)
print(lumins[compact_ncom][lumins[compact_ncom] <= 0],
lumins[compact_ncom][lumins[compact_ncom] <= 0].size)
try:
cbar = axes[i].hexbin(lumins[diffuse_com], hlrs[diffuse_com],
gridsize=50,
mincnt=1, C=w[diffuse_com],
reduce_C_function=np.sum,
xscale='log', yscale='log',
norm=weight_norm, linewidths=0.2,
cmap='Greys', extent=extent)
except ValueError as e:
print(e, "Diffuse complete", snap, f)
try:
axes[i].hexbin(lumins[compact_com],
hlrs[compact_com], gridsize=50,
mincnt=1,
C=w[compact_com],
reduce_C_function=np.sum,
xscale='log', yscale='log',
norm=weight_norm, linewidths=0.2,
cmap='plasma', extent=extent)
except ValueError as e:
print(e, "Compact complete", snap, f)
try:
popt, pcov = curve_fit(r_fit, lumins[complete],
hlrs[complete],
p0=(kawa_params['r_0'][7],
kawa_params['beta'][7]),
sigma=w[complete])
except ValueError as e:
print(e)
try:
popt1, pcov1 = curve_fit(r_fit, lumins[compact_com],
hlrs[compact_com],
p0=(kawa_params['r_0'][7],
kawa_params['beta'][7]),
sigma=w[compact_com])
except ValueError as e:
print(e)
try:
popt2, pcov2 = curve_fit(r_fit, lumins,
hlrs,
p0=(kawa_params['r_0'][7],
kawa_params['beta'][7]),
sigma=w)
except ValueError as e:
print(e)
try:
print("--------------", "Total", "No smooth", Type, mtype,
f, snap,
"--------------")
print("R_0=", popt[0], "+/-", np.sqrt(pcov[0, 0]))
print("beta=", popt[1], "+/-", np.sqrt(pcov[1, 1]))
# print("--------------", "Total", "Compact", mtype, f,
# "--------------")
# print("R_0=", popt1[0], "+/-", np.sqrt(pcov1[0, 0]))
# print("beta=", popt1[1], "+/-", np.sqrt(pcov1[1, 1]))
# print(pcov1)
print("--------------", "Kawamata", "All", mtype, f,
"--------------")
if int(z) in [6, 7, 8, 9]:
print("R_0=", kawa_params['r_0'][int(z)])
print("beta=", kawa_params['beta'][int(z)])
else:
print("R_0= ---")
print("beta= ---")
print(
"----------------------------------------------------------")
fit_lumins = np.logspace(np.log10(np.min(lumins[complete])),
np.log10(np.max(lumins[complete])),
1000)
fit = r_fit(fit_lumins, popt[0], popt[1])
axes[i].plot(fit_lumins, fit,
linestyle='-', color="r",
zorder=3)
# fit = r_fit(fit_lumins, popt1[0], popt1[1])
#
# axes[i].plot(fit_lumins, fit,
# linestyle='-', color="m",
# zorder=3,
# linewidth=2)
except ValueError as e:
print(e)
continue
if int(z) in [6, 7, 8, 9]:
if z == 7 or z == 6:
low_lim = -16
elif z == 8:
low_lim = -16.8
else:
low_lim = -15.4
fit_lumins = np.logspace(np.log10(M_to_lum(-21.6)),
np.log10(M_to_lum(low_lim)),
1000)
fit = kawa_fit(fit_lumins, kawa_params['r_0'][int(z)],
kawa_params['beta'][int(z)])
axes[i].plot(fit_lumins, fit,
linestyle='dashed', color="g",
zorder=2,
label="Kawamata+18")
# if int(z) in [7, 8, 9, 10, 11]:
#
# fit_lumins = np.logspace(28.5,
# bt_fit_uplims[int(z)],
# 1000)
#
# fit = r_fit(fit_lumins, bt_params['r_0'][int(z)],
# bt_params['beta'][int(z)])
# axes[i].plot(fit_lumins, fit,
# linestyle='dashed', color="b",
# zorder=2,
# label="Marshall+21")
#
# if int(z) in [5, 6, 7, 8, 9, 10]:
#
# fit_lumins = np.logspace(np.log10(M_to_lum(-22)),
# np.log10(M_to_lum(-14)),
# 1000)
#
# fit = r_fit(fit_lumins, mer_params['r_0'][int(z)],
# mer_params['beta'][int(z)])
# axes[i].plot(fit_lumins, fit,
# linestyle='dashed', color="m",
# zorder=2,
# label="Liu+17")
axes[i].text(0.95, 0.05, f'$z={z}$',
bbox=dict(boxstyle="round,pad=0.3", fc='w',
ec="k", lw=1, alpha=0.8),
transform=axes[i].transAxes,
horizontalalignment='right',
fontsize=8)
axes[i].tick_params(axis='both', which='both',
bottom=True, left=True)
# Label axes
axes[i].set_xlabel(r"$L_{" + f.split(".")[-1]
+ "}/$ [erg $/$ s $/$ Hz]")
axes[i].set_xlim(xlims[0], xlims[1])
for i in range(len(axes)):
axes[i].set_ylim(ylims[0], ylims[1])
axes[0].set_ylabel('$R_{1/2}/ [pkpc]$')
axes[0].tick_params(axis='y', which='both', left=True)
uni_legend_elements = []
# uni_legend_elements.append(
# Line2D([0], [0], color="m", linestyle="dotted",
# label="FLARES (All)"))
uni_legend_elements.append(
Line2D([0], [0], color="r", linestyle="-",
label="FLARES"))
# uni_legend_elements.append(
# Line2D([0], [0], color="m", linestyle="--",
# label="FLARES ($M_{\star}/M_\odot<10^{9}$)"))
uni_legend_elements.append(
Line2D([0], [0], color="g", linestyle="--",
label=labels["K18"]))
# uni_legend_elements.append(
# Line2D([0], [0], color="b", linestyle="--",
# label="Marshall+2021"))
# uni_legend_elements.append(
# Line2D([0], [0], color="m", linestyle="--",
# label="Liu+2017"))
included = []
for l in legend_elements:
if (l.get_label(), l.get_marker()) not in included:
print((l.get_label(), l.get_marker()))
uni_legend_elements.append(l)
included.append((l.get_label(), l.get_marker()))
axes[2].legend(handles=uni_legend_elements, loc='upper center',
bbox_to_anchor=(0.5, -0.15), fancybox=True,
ncol=len(uni_legend_elements))
fig.savefig(
'plots/FitHalfLightRadius_NoSmooth_' + mtype + "_" + f + '_'
+ orientation + '_' + Type + "_" + extinction + "_"
+ sample + ".png",
bbox_inches='tight', dpi=300)
plt.close(fig)
# Define Kawamata17 fit and parameters
kawa_params = {
'beta': {
6: 0.46,
7: 0.46,
8: 0.38,
9: 0.56
},
'r_0': {
6: 0.94,
7: 0.94,
8: 0.81,
9: 1.2
}
}
kawa_fit = lambda l, r0, b: r0 * (l / M_to_lum(-21))**b
ono_fit = lambda z, a, m: 10**(m * np.log10(1 + z) + a)
L_star = M_to_lum(-21)
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 M_to_m(M, cosmo, z):
lum = photconv.M_to_lum(M)
flux = photconv.lum_to_flux(lum, cosmo, z)
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