def process_single_halo(self,
zoom_obj: Zoom = None,
path_to_snap: str = None,
path_to_catalogue: str = None,
**kwargs):
sw_data, vr_data = self.get_handles_from_zoom(zoom_obj, path_to_snap,
path_to_catalogue,
**kwargs)
r500 = vr_data.spherical_overdensities.r_500_rhocrit[0].to('Mpc')
r2500 = vr_data.spherical_overdensities.r_2500_rhocrit[0].to('Mpc')
sw_data.gas.radial_distances.convert_to_physical()
sw_data.gas.temperatures.convert_to_physical()
sw_data.gas.masses.convert_to_physical()
# Compute hydrogen number density and the log10
# of the temperature to provide to the xray interpolator.
data_nH = np.log10(sw_data.gas.element_mass_fractions.hydrogen *
sw_data.gas.densities.to('g*cm**-3') / mp)
data_T = np.log10(sw_data.gas.temperatures.value)
# Interpolate the Cloudy table to get emissivities
emissivities = cloudy.interpolate_X_Ray(
data_nH, data_T, sw_data.gas.element_mass_fractions)
# Select hot gas within sphere
mask = np.where((sw_data.gas.radial_distances <= r500)
& (sw_data.gas.temperatures > Tcut_halogas)
& (sw_data.gas.fofgroup_ids == 1))[0]
LX500 = self.get_emissivities(sw_data, emissivities, mask)
mask = np.where((sw_data.gas.radial_distances <= r2500)
& (sw_data.gas.temperatures > Tcut_halogas)
& (sw_data.gas.fofgroup_ids == 1))[0]
LX2500 = self.get_emissivities(sw_data, emissivities, mask)
# Select hot gas within spherical shell (no core)
mask = np.where((sw_data.gas.radial_distances >= 0.15 * r500)
& (sw_data.gas.radial_distances <= r500)
& (sw_data.gas.temperatures > Tcut_halogas)
& (sw_data.gas.fofgroup_ids == 1))[0]
LX500_nocore = self.get_emissivities(sw_data, emissivities, mask)
mask = np.where((sw_data.gas.radial_distances >= 0.15 * r500)
& (sw_data.gas.radial_distances <= r2500)
& (sw_data.gas.temperatures > Tcut_halogas)
& (sw_data.gas.fofgroup_ids == 1))[0]
LX2500_nocore = self.get_emissivities(sw_data, emissivities, mask)
return LX500, LX2500, LX500_nocore, LX2500_nocore
def process_single_halo(
self,
zoom_obj: Zoom = None,
path_to_snap: str = None,
path_to_catalogue: str = None
):
sw_data, vr_data = self.get_handles_from_zoom(
zoom_obj,
path_to_snap,
path_to_catalogue,
mask_radius_r500=self.max_radius_r500
)
fb = Cosmology().fb0
critical_density = unyt_quantity(
sw_data.metadata.cosmology.critical_density(sw_data.metadata.z).value, 'g/cm**3'
).to('Msun/Mpc**3')
sw_data.gas.radial_distances.convert_to_physical()
sw_data.gas.masses.convert_to_physical()
sw_data.gas.densities.convert_to_physical()
sw_data.gas.entropies.convert_to_physical()
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(f'[{self.__class__.__name__}] {err}')
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
)
true_hse.interpolate_hse(density_contrast=500.)
r500 = true_hse.r500hse
m500 = true_hse.m500hse
try:
_ = sw_data.gas.temperatures
except AttributeError as err:
print(f'[{self.__class__.__name__}] {err}')
if xlargs.debug:
print(f"[{self.__class__.__name__}] Computing gas temperature from internal energies.")
sw_data.gas.temperatures = sw_data.gas.internal_energies * (gamma - 1) * mean_molecular_weight * mh / kb
try:
_ = sw_data.gas.fofgroup_ids
except AttributeError as err:
print(f'[{self.__class__.__name__}] {err}')
if xlargs.debug:
print(f"[{self.__class__.__name__}] Select particles only by radial distance.")
sw_data.gas.fofgroup_ids = np.ones_like(sw_data.gas.densities)
index = np.where(
(sw_data.gas.radial_distances < self.max_radius_r500 * r500) &
(sw_data.gas.fofgroup_ids == 1) &
(sw_data.gas.temperatures > Tcut_halogas)
)[0]
radial_distance = sw_data.gas.radial_distances[index] / r500
# Define radial bins and shell volumes
lbins = np.logspace(-2, np.log10(self.max_radius_r500), 51) * radial_distance.units
radial_bin_centres = 10 ** (0.5 * np.log10(lbins[1:] * lbins[:-1])) * radial_distance.units
if self.weighting == 'xray':
# Compute hydrogen number density and the log10
# of the temperature to provide to the xray interpolator.
data_nH = np.log10(
sw_data.gas.element_mass_fractions.hydrogen * sw_data.gas.densities.to('g*cm**-3') / mp)
data_T = np.log10(sw_data.gas.temperatures.value)
# Interpolate the Cloudy table to get emissivities
emissivities = unyt_array(
10 ** cloudy.interpolate_X_Ray(
data_nH,
data_T,
sw_data.gas.element_mass_fractions,
fill_value=-50.
), 'erg/s/cm**3'
)
xray_luminosities = emissivities * sw_data.gas.masses / sw_data.gas.densities
weighting = xray_luminosities[index]
del data_nH, data_T, emissivities, xray_luminosities
elif self.weighting == 'mass':
weighting = sw_data.gas.masses[index]
elif self.weighting == 'volume':
volume_proxy = sw_data.gas.masses[index] / sw_data.gas.densities[index]
weighting = volume_proxy
del volume_proxy
gas_mass = sw_data.gas.masses[index]
gas_mass_profile = histogram_unyt(
radial_distance,
bins=lbins,
weights=gas_mass,
)
gas_mass_profile = np.nancumsum(gas_mass_profile.value) * gas_mass_profile.units
gas_mass_profile.convert_to_units(Solar_Mass)
return radial_bin_centres, gas_mass_profile, m500