def plot_observations(self, axes: plt.Axes):
sun_observations = Sun2009()
r_r500, S_S500_50, S_S500_10, S_S500_90 = sun_observations.get_shortcut(
)
rexcess = Pratt2010(n_radial_bins=21)
bin_median, bin_perc16, bin_perc84 = rexcess.combine_entropy_profiles(
m500_limits=(1e14 * Solar_Mass, 5e14 * Solar_Mass),
k500_rescale=True)
r = np.array([*axes.get_xlim()])
k = 1.40 * r**1.1
axes.plot(r, k, c='grey', ls='--', label='VKB (2005)')
asymmetric_error = np.array(
list(zip(bin_median - bin_perc16, bin_perc84 - bin_median))).T
axes.errorbar(rexcess.radial_bins,
bin_median,
yerr=asymmetric_error,
fmt='o',
markersize=2,
color='grey',
ecolor='lightgray',
elinewidth=0.7,
capsize=0,
label=rexcess.citation)
asymmetric_error = np.array(
list(zip(S_S500_50 - S_S500_10, S_S500_90 - S_S500_50))).T
axes.errorbar(r_r500,
S_S500_50,
yerr=asymmetric_error,
fmt='^',
markersize=2,
color='grey',
ecolor='lightgray',
elinewidth=0.7,
capsize=0,
label=sun_observations.citation)
entropy_profile * gas_fraction_enclosed**(2 / 3) *
cosmology.ez_function(gas_profile_obj.z)**(2 / 3),
linestyle='-',
color='r',
linewidth=1,
alpha=1,
)
axes[1, 1].set_xscale('log')
axes[1, 1].set_yscale('log')
axes[1, 1].set_ylabel(
r'$E(z) ^ {2/3}~(K/K_{500})~\times~f_{\rm gas}(<r) ^ {2/3}$')
axes[1, 1].set_xlabel(r'$r/r_{500}$')
axes[1, 1].set_ylim([5e-5, 3])
axes[1, 1].set_xlim([0.01, 1])
sun_observations = Sun2009()
r_r500, S_S500_50, S_S500_10, S_S500_90 = sun_observations.get_shortcut()
axes[0, 1].errorbar(r_r500,
S_S500_50,
yerr=(S_S500_50 - S_S500_10, S_S500_90 - S_S500_50),
fmt='o',
color='grey',
ecolor='lightgray',
elinewidth=0.5,
capsize=0,
markersize=2,
label=sun_observations.citation,
zorder=0)
rexcess = Pratt2010(n_radial_bins=30)
import velociraptor as vr
from typing import Tuple
import matplotlib.patheffects as path_effects
try:
plt.style.use("../mnras.mplstyle")
except:
pass
import sys
# Make the register backend visible to the script
sys.path.append("..")
from literature import Sun2009, Cosmology
Sun2009 = Sun2009()
fb = Cosmology().fb
# Constants
mean_molecular_weight = 0.59
mean_atomic_weight_per_free_electron = 1.14
def update_prop(handle, orig):
handle.update_from(orig)
handle.set_marker("o")
def histogram_unyt(
data: unyt.unyt_array,
bins: unyt.unyt_array = None,
import sys
import numpy as np
from unyt import kpc
from matplotlib import pyplot as plt
sys.path.append("..")
from literature import Sun2009, Pratt2010
plt.style.use('../register/mnras.mplstyle')
lit = Sun2009(reduced_table=True, disable_cosmo_conversion=False)
from matplotlib import pyplot as plt
x = [
np.median(30 * kpc / lit.R_500),
np.median(0.15),
np.median(lit.R_2500 / lit.R_500),
np.median(lit.R_1500 / lit.R_500),
np.median(lit.R_1000 / lit.R_500),
np.median(1),
]
y = [
np.median(lit.K_500 / lit.K_500_adi),
np.median(lit.K_1000 / lit.K_500_adi),
np.median(lit.K_1500 / lit.K_500_adi),
np.median(lit.K_2500 / lit.K_500_adi),
np.median(lit.K_0p15r500 / lit.K_500_adi),
np.median(lit.K_30kpc / lit.K_500_adi),
][::-1]
plt.loglog()
plt.scatter(x, y, label='Edo')
def process_single_halo(
self,
zoom_obj: Zoom = None,
path_to_snap: str = None,
path_to_catalogue: str = None,
agn_time: str = None,
z_agn_start: float = 18,
z_agn_end: float = 0.,
):
aperture_fraction = xlargs.aperture_percent / 100
sw_data, vr_data = self.get_handles_from_zoom(zoom_obj,
path_to_snap,
path_to_catalogue,
mask_radius_r500=5)
try:
m500 = vr_data.spherical_overdensities.mass_500_rhocrit[0].to(
'Msun')
r500 = vr_data.spherical_overdensities.r_500_rhocrit[0].to('Mpc')
except AttributeError as err:
print(err)
print(
f'[{self.__class__.__name__}] Launching spherical overdensity calculation...'
)
spherical_overdensity = SODelta500(
path_to_snap=path_to_snap,
path_to_catalogue=path_to_catalogue,
)
m500 = spherical_overdensity.get_m500()
r500 = spherical_overdensity.get_r500()
if xlargs.mass_estimator == 'hse':
true_hse = HydrostaticEstimator(
path_to_catalogue=path_to_catalogue,
path_to_snap=path_to_snap,
profile_type='true',
diagnostics_on=False).interpolate_hse()
r500 = true_hse.r500hse
m500 = true_hse.M500hse
aperture_fraction = aperture_fraction * r500
# Convert datasets to physical quantities
# r500c is already in physical units
sw_data.gas.radial_distances.convert_to_physical()
sw_data.gas.coordinates.convert_to_physical()
sw_data.gas.masses.convert_to_physical()
sw_data.gas.densities.convert_to_physical()
activate_cooling_times = True
try:
cooling_times = calculate_mean_cooling_times(sw_data)
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f'[{self.__class__.__name__}] Setting activate_cooling_times = False'
)
activate_cooling_times = False
if xlargs.debug:
print(f"[{self.__class__.__name__}] m500 = ", m500)
print(f"[{self.__class__.__name__}] r500 = ", r500)
print(f"[{self.__class__.__name__}] aperture_fraction = ",
aperture_fraction)
print(
f"[{self.__class__.__name__}] Number of particles being imported",
len(sw_data.gas.densities))
gamma = 5 / 3
try:
a_heat = sw_data.gas.last_agnfeedback_scale_factors
except AttributeError as err:
print(err)
if xlargs.debug:
print('Setting `last_agnfeedback_scale_factors` with 0.1.')
a_heat = np.ones_like(sw_data.gas.masses) * 0.1
try:
fof_ids = sw_data.gas.fofgroup_ids
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Select particles only by radial distance."
)
fof_ids = np.ones_like(sw_data.gas.densities)
try:
temperature = sw_data.gas.temperatures
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Computing gas temperature from internal energies."
)
A = sw_data.gas.entropies * sw_data.units.mass
temperature = mean_molecular_weight * (gamma - 1) * (
A * sw_data.gas.densities**(5 / 3 - 1)) / (gamma - 1) * mh / kb
try:
hydrogen_fractions = sw_data.gas.element_mass_fractions.hydrogen
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Setting H fractions to primordial values."
)
hydrogen_fractions = np.ones_like(
sw_data.gas.densities) * primordial_hydrogen_mass_fraction
try:
agn_flag = sw_data.gas.heated_by_agnfeedback
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Setting all agn_flag to zero."
)
agn_flag = np.zeros_like(sw_data.gas.densities)
try:
snii_flag = sw_data.gas.heated_by_sniifeedback
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Setting all snii_flag to zero."
)
snii_flag = np.zeros_like(sw_data.gas.densities)
if agn_time is None:
index = np.where((sw_data.gas.radial_distances < aperture_fraction)
& (fof_ids == 1) & (temperature > 1e5))[0]
number_density = (sw_data.gas.densities / mh).to(
'cm**-3').value[index] * hydrogen_fractions[index]
temperature = temperature.to('K').value[index]
elif agn_time == 'before':
index = np.where(
(sw_data.gas.radial_distances < aperture_fraction)
& (fof_ids == 1) & (a_heat > (1 / (z_agn_start + 1)))
& (a_heat < (1 / (z_agn_end + 1)))
& (sw_data.gas.densities_before_last_agnevent > 0))[0]
density = sw_data.gas.densities_before_last_agnevent[index]
number_density = (
density / mh).to('cm**-3').value * hydrogen_fractions[index]
A = sw_data.gas.entropies_before_last_agnevent[
index] * sw_data.units.mass
temperature = mean_molecular_weight * (gamma - 1) * (
A * density**(5 / 3 - 1)) / (gamma - 1) * mh / kb
temperature = temperature.to('K').value
elif agn_time == 'after':
index = np.where((sw_data.gas.radial_distances < aperture_fraction)
& (fof_ids == 1) & (a_heat > (1 /
(z_agn_start + 1)))
& (a_heat < (1 / (z_agn_end + 1)))
& (sw_data.gas.densities_at_last_agnevent > 0))[0]
density = sw_data.gas.densities_at_last_agnevent[index]
number_density = (
density / mh).to('cm**-3').value * hydrogen_fractions[index]
A = sw_data.gas.entropies_at_last_agnevent[
index] * sw_data.units.mass
temperature = mean_molecular_weight * (gamma - 1) * (
A * density**(5 / 3 - 1)) / (gamma - 1) * mh / kb
temperature = temperature.to('K').value
agn_flag = agn_flag[index]
snii_flag = snii_flag[index]
agn_flag = agn_flag > 0
snii_flag = snii_flag > 0
# Calculate the critical density for the cross-hair marker
rho_crit = unyt_quantity(
sw_data.metadata.cosmology.critical_density(
sw_data.metadata.z).value, 'g/cm**3').to('Msun/Mpc**3')
nH_500 = (primordial_hydrogen_mass_fraction * Cosmology().fb0 *
rho_crit * 500 / mh).to('cm**-3')
# Entropy
electron_number_density = (sw_data.gas.densities[index] /
mh).to('cm**-3') / mean_molecular_weight
entropy = kb * temperature * K / electron_number_density**(2 / 3)
entropy = entropy.to('keV*cm**2')
x = number_density
y = temperature
if activate_cooling_times:
w = cooling_times[index]
if xlargs.debug:
print("Number of particles being plotted", len(x))
# Set the limits of the figure.
assert (x > 0).all(), f"Found negative value(s) in x: {x[x <= 0]}"
assert (y > 0).all(), f"Found negative value(s) in y: {y[y <= 0]}"
# density_bounds = [1e-6, 1e4] # in nh/cm^3
# temperature_bounds = [1e3, 1e10] # in K
density_bounds = [1e-5, 1] # in nh/cm^3
temperature_bounds = [1e6, 1e9] # in K
pdf_ybounds = [1, 10**6]
bins = 256
# Make the norm object to define the image stretch
density_bins = np.logspace(np.log10(density_bounds[0]),
np.log10(density_bounds[1]), bins)
temperature_bins = np.logspace(np.log10(temperature_bounds[0]),
np.log10(temperature_bounds[1]), bins)
T500 = (G * mean_molecular_weight * m500 * mp / r500 / 2 /
kb).to('K').value
K500 = (T500 * K * kb /
(3 * m500 * Cosmology().fb0 /
(4 * np.pi * r500**3 * mp))**(2 / 3)).to('keV*cm**2')
# Make the norm object to define the image stretch
contour_density_bins = np.logspace(
np.log10(density_bounds[0]) - 0.5,
np.log10(density_bounds[1]) + 0.5, bins * 4)
contour_temperature_bins = np.logspace(
np.log10(temperature_bounds[0]) - 0.5,
np.log10(temperature_bounds[1]) + 0.5, bins * 4)
fig = plt.figure(figsize=(8, 6), constrained_layout=True)
gs = fig.add_gridspec(3, 4, hspace=0.35, wspace=0.7)
axes = gs.subplots()
for ax in [axes[0, 0], axes[0, 1], axes[0, 2], axes[1, 0], axes[1, 1]]:
ax.loglog()
# Draw cross-hair marker
ax.hlines(y=T500,
xmin=nH_500 / 3,
xmax=nH_500 * 3,
colors='k',
linestyles='-',
lw=0.5)
ax.vlines(x=nH_500,
ymin=T500 / 5,
ymax=T500 * 5,
colors='k',
linestyles='-',
lw=0.5)
# Draw contours
draw_k500(ax, contour_density_bins, contour_temperature_bins, K500)
draw_adiabats(ax, contour_density_bins, contour_temperature_bins)
draw_cooling_contours(ax,
contour_density_bins,
contour_temperature_bins,
levels=[1, 1e2, 1e3, 1e4, 1e5],
color='green')
draw_cooling_contours(
ax,
contour_density_bins,
contour_temperature_bins,
levels=[Cosmology().age(sw_data.metadata.z).to('Myr').value],
prefix='$t_H(z)=$',
color='red',
use_labels=False)
# PLOT ALL PARTICLES ===============================================
H, density_edges, temperature_edges = np.histogram2d(
x, y, bins=[density_bins, temperature_bins])
draw_2d_hist(axes[0, 0], density_edges, temperature_edges, H,
'Greys_r', "All particles")
# PLOT SN HEATED PARTICLES ===============================================
H, density_edges, temperature_edges = np.histogram2d(
x[(snii_flag & ~agn_flag)],
y[(snii_flag & ~agn_flag)],
bins=[density_bins, temperature_bins])
draw_2d_hist(axes[0, 1], density_edges, temperature_edges, H,
'Greens_r', "SNe heated only")
axes[0, 1].axhline(10**7.5, color='k', linestyle='--', lw=1, zorder=0)
# PLOT NOT HEATED PARTICLES ===============================================
H, density_edges, temperature_edges = np.histogram2d(
x[(~snii_flag & ~agn_flag)],
y[(~snii_flag & ~agn_flag)],
bins=[density_bins, temperature_bins])
draw_2d_hist(axes[0, 2], density_edges, temperature_edges, H,
'Greens_r', "Not heated")
# PLOT AGN HEATED PARTICLES ===============================================
H, density_edges, temperature_edges = np.histogram2d(
x[(agn_flag & ~snii_flag)],
y[(agn_flag & ~snii_flag)],
bins=[density_bins, temperature_bins])
draw_2d_hist(axes[1, 1], density_edges, temperature_edges, H, 'Reds_r',
"AGN heated only")
axes[1, 1].axhline(10**8.5, color='k', linestyle='--', lw=1, zorder=0)
# PLOT AGN+SN HEATED PARTICLES ===============================================
H, density_edges, temperature_edges = np.histogram2d(
x[(agn_flag & snii_flag)],
y[(agn_flag & snii_flag)],
bins=[density_bins, temperature_bins])
draw_2d_hist(axes[1, 0], density_edges, temperature_edges, H,
'Purples_r', "AGN and SNe heated")
axes[1, 0].axhline(10**8.5, color='k', linestyle='--', lw=1, zorder=0)
axes[1, 0].axhline(10**7.5, color='k', linestyle='--', lw=1, zorder=0)
bins = np.linspace(0., 5.5, 51)
axes[2, 0].clear()
axes[2, 0].set_xscale('linear')
axes[2, 0].set_yscale('log')
if activate_cooling_times:
axes[2, 0].hist(w, bins=bins, histtype='step', label='All')
axes[2, 0].hist(w[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[2, 0].hist(w[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[2, 0].hist(w[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[2, 0].hist(w[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[2, 0].axvline(np.log10(Cosmology().age(
sw_data.metadata.z).to('Myr').value),
color='k',
linestyle='--',
lw=0.5,
zorder=0)
axes[2, 0].set_xlabel(f"$\log_{{10}}$(Cooling time [Myr])")
axes[2, 0].set_ylabel('Number of particles')
axes[2, 0].set_ylim(pdf_ybounds)
axes[2, 0].legend(loc="upper left")
if activate_cooling_times:
hydrogen_fraction = sw_data.gas.element_mass_fractions.hydrogen[
index]
bins = np.linspace(0, 1, 51)
axes[2, 1].clear()
axes[2, 1].set_xscale('linear')
axes[2, 1].set_yscale('log')
if activate_cooling_times:
axes[2, 1].hist(hydrogen_fraction,
bins=bins,
histtype='step',
label='All')
axes[2, 1].hist(hydrogen_fraction[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[2, 1].hist(hydrogen_fraction[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[2, 1].hist(hydrogen_fraction[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[2, 1].hist(hydrogen_fraction[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[2, 1].set_xlabel("Hydrogen fraction")
axes[2, 1].set_ylabel('Number of particles')
axes[2, 1].set_ylim(pdf_ybounds)
if activate_cooling_times:
log_gas_Z = np.log10(
sw_data.gas.metal_mass_fractions.value[index] / 0.0133714)
bins = np.linspace(-4, 1, 51)
axes[2, 2].clear()
axes[2, 2].set_xscale('linear')
axes[2, 2].set_yscale('log')
if activate_cooling_times:
axes[2, 2].hist(log_gas_Z, bins=bins, histtype='step', label='All')
axes[2, 2].hist(log_gas_Z[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[2, 2].hist(log_gas_Z[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[2, 2].hist(log_gas_Z[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[2, 2].hist(log_gas_Z[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[2, 2].axvline(0.5, color='k', linestyle='--', lw=0.5, zorder=0)
axes[2, 2].set_xlabel(f"$\log_{{10}}$(Metallicity [Z$_\odot$])")
axes[2, 2].set_ylabel('Number of particles')
axes[2, 2].set_ylim(pdf_ybounds)
bins = np.logspace(np.log10(density_edges.min()),
np.log10(density_edges.max()), 51)
axes[0, 3].clear()
axes[0, 3].set_xscale('log')
axes[0, 3].set_yscale('log')
axes[0, 3].hist(x, bins=bins, histtype='step', label='All')
axes[0, 3].hist(x[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[0, 3].hist(x[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[0, 3].hist(x[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[0, 3].hist(x[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[0, 3].set_xlabel(f"Density [$n_H$ cm$^{{-3}}$]")
axes[0, 3].set_ylabel('Number of particles')
axes[0, 3].set_ylim(pdf_ybounds)
axes[0, 3].legend()
bins = np.logspace(np.log10(temperature_edges.min()),
np.log10(temperature_edges.max()), 51)
axes[1, 3].clear()
axes[1, 3].set_xscale('log')
axes[1, 3].set_yscale('log')
axes[1, 3].hist(y, bins=bins, histtype='step', label='All')
axes[1, 3].hist(y[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[1, 3].hist(y[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[1, 3].hist(y[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[1, 3].hist(y[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[1, 3].set_xlabel("Temperature [K]")
axes[1, 3].set_ylabel('Number of particles')
axes[1, 3].set_ylim(pdf_ybounds)
bins = np.logspace(0, 4, 51)
axes[2, 3].clear()
axes[2, 3].set_xscale('log')
axes[2, 3].set_yscale('log')
axes[2, 3].hist(entropy, bins=bins, histtype='step', label='All')
axes[2, 3].hist(entropy[(agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='AGN & SN')
axes[2, 3].hist(entropy[(agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='AGN')
axes[2, 3].hist(entropy[(~agn_flag & snii_flag)],
bins=bins,
histtype='step',
label='SN')
axes[2, 3].hist(entropy[(~agn_flag & ~snii_flag)],
bins=bins,
histtype='step',
label='Not heated')
axes[2, 3].set_xlabel("Entropy [keV cm$^2$]")
axes[2, 3].set_ylabel('Number of particles')
axes[2, 3].set_ylim(pdf_ybounds)
# Entropy profile
max_radius_r500 = 4
try:
temperature = sw_data.gas.temperatures
except AttributeError as err:
print(err)
if xlargs.debug:
print(
f"[{self.__class__.__name__}] Computing gas temperature from internal energies."
)
A = sw_data.gas.entropies * sw_data.units.mass
temperature = mean_molecular_weight * (gamma - 1) * (
A * sw_data.gas.densities**(5 / 3 - 1)) / (gamma - 1) * mh / kb
index = np.where((sw_data.gas.radial_distances < max_radius_r500 *
r500) & (fof_ids == 1) & (temperature > 1e5))[0]
radial_distance = sw_data.gas.radial_distances[index] / r500
sw_data.gas.masses = sw_data.gas.masses[index]
temperature = temperature[index]
# Define radial bins and shell volumes
lbins = np.logspace(-2, np.log10(max_radius_r500),
51) * radial_distance.units
radial_bin_centres = 10.0**(
0.5 * np.log10(lbins[1:] * lbins[:-1])) * radial_distance.units
volume_shell = (4. * np.pi / 3.) * (r500**3) * ((lbins[1:])**3 -
(lbins[:-1])**3)
mass_weights, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=sw_data.gas.masses)
mass_weights[mass_weights == 0] = np.nan # Replace zeros with Nans
density_profile = mass_weights / volume_shell
number_density_profile = (density_profile.to('g/cm**3') /
(mp * mean_molecular_weight)).to('cm**-3')
mass_weighted_temperatures = (temperature *
kb).to('keV') * sw_data.gas.masses
temperature_weights, _ = histogram_unyt(
radial_distance, bins=lbins, weights=mass_weighted_temperatures)
temperature_weights[temperature_weights ==
0] = np.nan # Replace zeros with Nans
temperature_profile = temperature_weights / mass_weights # kBT in units of [keV]
entropy_profile = temperature_profile / number_density_profile**(2 / 3)
rho_crit = unyt_quantity(
sw_data.metadata.cosmology.critical_density(
sw_data.metadata.z).value, 'g/cm**3').to('Msun/Mpc**3')
density_profile /= rho_crit
kBT500 = (G * mean_molecular_weight * m500 * mp / r500 / 2).to('keV')
K500 = (kBT500 / (3 * m500 * Cosmology().fb0 /
(4 * np.pi * r500**3 * mp))**(2 / 3)).to('keV*cm**2')
axes[1, 2].plot(
radial_bin_centres,
entropy_profile / K500,
linestyle='-',
color='r',
linewidth=1,
alpha=1,
)
axes[1, 2].set_xscale('log')
axes[1, 2].set_yscale('log')
axes[1, 2].axvline(0.15, color='k', linestyle='--', lw=0.5, zorder=0)
axes[1, 2].set_ylabel(r'Entropy [keV cm$^2$]')
axes[1, 2].set_xlabel(r'$r/r_{500}$')
# axes[1, 2].set_ylim([1, 1e4])
axes[1, 2].set_ylim([1e-2, 5])
axes[1, 2].set_xlim([0.01, max_radius_r500])
# axes[1, 2].axhline(y=K500, color='k', linestyle=':', linewidth=0.5)
# axes[1, 2].text(
# axes[1, 2].get_xlim()[0], K500, r'$K_{500}$',
# horizontalalignment='left',
# verticalalignment='bottom',
# color='k',
# bbox=dict(
# boxstyle='square,pad=10',
# fc='none',
# ec='none'
# )
# )
sun_observations = Sun2009()
sun_observations.filter_by('M_500', 8e13, 3e14)
sun_observations.overlay_entropy_profiles(axes=axes[1, 2],
k_units='K500adi',
markersize=1,
linewidth=0.5)
rexcess = Pratt2010()
bin_median, bin_perc16, bin_perc84 = rexcess.combine_entropy_profiles(
m500_limits=(1e14 * Solar_Mass, 5e14 * Solar_Mass),
k500_rescale=True)
axes[1, 2].fill_between(rexcess.radial_bins,
bin_perc16,
bin_perc84,
color='aqua',
alpha=0.85,
linewidth=0)
axes[1, 2].plot(rexcess.radial_bins, bin_median, c='k')
z_agn_recent_text = (
f"Selecting gas heated between {z_agn_start:.1f} > z > {z_agn_end:.1f} (relevant to AGN plot only)\n"
f"({1 / (z_agn_start + 1):.2f} < a < {1 / (z_agn_end + 1):.2f})\n")
if agn_time is not None:
z_agn_recent_text = (
f"Selecting gas {agn_time:s} heated between {z_agn_start:.1f} > z > {z_agn_end:.1f}\n"
f"({1 / (z_agn_start + 1):.2f} < a < {1 / (z_agn_end + 1):.2f})\n"
)
fig.suptitle((
f"{os.path.basename(path_to_snap)}\n"
f"Aperture = {xlargs.aperture_percent / 100:.2f} $R_{{500}}$\t\t"
f"$z = {sw_data.metadata.z:.2f}$\t\t"
f"Age = {Cosmology().age(sw_data.metadata.z).value:.2f} Gyr\t\t"
f"\t$M_{{500}}={latex_float(m500.value)}\\ {m500.units.latex_repr}$\n"
f"{z_agn_recent_text:s}"
f"Central FoF group only\t\tEstimator: {xlargs.mass_estimator}"),
fontsize=7)
if not xlargs.quiet:
plt.show()
fig.savefig(os.path.join(
default_output_directory,
f"cooling_times_{os.path.basename(path_to_snap)[:-5].replace('.', 'p')}.png"
),
dpi=300)
plt.close()