def calc_BTF(model, gals):
"""
Calculates the baryonic Tully-Fisher relation for spiral galaxies in the given set of
galaxies.
The number of galaxies added to ``Model.properties["BTF_mass"]`` and
``Model.properties["BTF_vel"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size`
weighted by ``number_spirals_passed /``
:py:attr:`~sage_analysis.model.Model.num_gals_all_files`. If this value is greater
than ``number_spirals_passed``, then all spiral galaxies will be used.
"""
# Make sure we're getting spiral galaxies. That is, don't include galaxies
# that are too bulgy.
spirals = np.where((gals["Type"][:] == 0) & (gals["StellarMass"][:] + gals["ColdGas"][:] > 0.0) & \
(gals["StellarMass"][:] > 0.0) & (gals["ColdGas"][:] > 0.0) & \
(gals["BulgeMass"][:] / gals["StellarMass"][:] > 0.1) & \
(gals["BulgeMass"][:] / gals["StellarMass"][:] < 0.5))[0]
# Select a random subset of galaxies (if necessary).
spirals = select_random_indices(spirals, model.num_gals_all_files, model.sample_size)
baryon_mass = np.log10((gals["StellarMass"][:][spirals] + gals["ColdGas"][:][spirals]) * 1.0e10 / model.hubble_h)
velocity = np.log10(gals["Vmax"][:][spirals])
model.properties["BTF_mass"] = np.append(model.properties["BTF_mass"], baryon_mass)
model.properties["BTF_vel"] = np.append(model.properties["BTF_vel"], velocity)
def calc_reservoirs(model, gals):
"""
Calculates the mass in each reservoir as a function of halo virial mass.
The number of galaxies added to ``Model.properties["reservoir_mvir"]`` and
``Model.properties["reservoir_<reservoir_name>"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size` weighted by
``number_centrals_passed /`` :py:attr:`~sage_analysis.model.Model.num_gals_all_files`.
If this value is greater than ``number_centrals_passed``, then all central galaxies
will be used.
"""
# To reduce scatter, only use galaxies in halos with mass > 1.0e10 Msun/h.
centrals = np.where((gals["Type"][:] == 0) & (gals["Mvir"][:] > 1.0) & \
(gals["StellarMass"][:] > 0.0))[0]
# Select a random subset of galaxies (if necessary).
centrals = select_random_indices(centrals, model.num_gals_all_files, model.sample_size)
reservoirs = ["Mvir", "StellarMass", "ColdGas", "HotGas",
"EjectedMass", "IntraClusterStars"]
attribute_names = ["mvir", "stars", "cold", "hot", "ejected", "ICS"]
for (reservoir, attribute_name) in zip(reservoirs, attribute_names):
mass = np.log10(gals[reservoir][:][centrals] * 1.0e10 / model.hubble_h)
# Extend the previous list of masses with these new values.
dict_key = "reservoir_{0}".format(attribute_name)
model.properties[dict_key] = np.append(model.properties[dict_key], mass)
def calc_bh_bulge(model, gals):
"""
Calculates the black hole mass as a function of bulge mass.
The number of galaxies added to ``Model.properties["BlackHoleMass"]`` and
``Model.properties["BulgeMass"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size` weighted by
``number_galaxies_passed /`` :py:attr:`~sage_analysis.model.Model.num_gals_all_files`.
If this value is greater than ``number_galaxies_passed``, then all galaxies
will be used.
Notes
-----
We only consider galaxies with bulge mass greater than 10^8 Msun/h and a black
hole mass greater than 10^5 Msun/h.
"""
# Only care about galaxies that have appreciable masses.
my_gals = np.where((gals["BulgeMass"][:] > 0.01) & (gals["BlackHoleMass"][:] > 0.00001))[0]
# Select a random subset of galaxies (if necessary).
my_gals = select_random_indices(my_gals, model.num_gals_all_files, model.sample_size)
bh = np.log10(gals["BlackHoleMass"][:][my_gals] * 1.0e10 / model.hubble_h)
bulge = np.log10(gals["BulgeMass"][:][my_gals] * 1.0e10 / model.hubble_h)
model.properties["bh_mass"] = np.append(model.properties["bh_mass"], bh)
model.properties["bulge_mass"] = np.append(model.properties["bulge_mass"], bulge)
def calc_metallicity(model, gals):
"""
Calculates the metallicity as a function of stellar mass.
The number of galaxies added to ``Model.properties["metallicity_mass"]`` and
``Model.properties["metallicity"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size` weighted by
``number_centrals_passed /`` :py:attr:`~sage_analysis.model.Model.num_gals_all_files`.
If this value is greater than ``number_centrals_passed``, then all central galaxies
will be used.
"""
# Only care about central galaxies (Type 0) that have appreciable mass.
centrals = np.where((gals["Type"][:] == 0) & \
(gals["ColdGas"][:] / (gals["StellarMass"][:] + gals["ColdGas"][:]) > 0.1) & \
(gals["StellarMass"][:] > 0.01))[0]
# Select a random subset of galaxies (if necessary).
centrals = select_random_indices(centrals, model.num_gals_all_files, model.sample_size)
stellar_mass = np.log10(gals["StellarMass"][:][centrals] * 1.0e10 / model.hubble_h)
Z = np.log10((gals["MetalsColdGas"][:][centrals] / gals["ColdGas"][:][centrals]) / 0.02) + 9.0
model.properties["metallicity_mass"] = np.append(model.properties["metallicity_mass"], stellar_mass)
model.properties["metallicity"] = np.append(model.properties["metallicity"], Z)
def calc_gas_fraction(model, gals):
"""
Calculates the fraction of baryons that are in the cold gas reservoir as a function of
stellar mass.
The number of galaxies added to ``Model.properties["gas_frac_mass"]`` and
``Model.properties["gas_frac"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size`
weighted by ``number_spirals_passed /``
:py:attr:`~sage_analysis.model.Model.num_gals_all_files`. If this value is greater
than ``number_spirals_passed``, then all spiral galaxies will be used.
"""
# Make sure we're getting spiral galaxies. That is, don't include galaxies
# that are too bulgy.
spirals = np.where((gals["Type"][:] == 0) & (gals["StellarMass"][:] + gals["ColdGas"][:] > 0.0) &
(gals["BulgeMass"][:] / gals["StellarMass"][:] > 0.1) & \
(gals["BulgeMass"][:] / gals["StellarMass"][:] < 0.5))[0]
# Select a random subset of galaxies (if necessary).
spirals = select_random_indices(spirals, model.num_gals_all_files, model.sample_size)
stellar_mass = np.log10(gals["StellarMass"][:][spirals] * 1.0e10 / model.hubble_h)
gas_fraction = gals["ColdGas"][:][spirals] / (gals["StellarMass"][:][spirals] + gals["ColdGas"][:][spirals])
model.properties["gas_frac_mass"] = np.append(model.properties["gas_frac_mass"], stellar_mass)
model.properties["gas_frac"] = np.append(model.properties["gas_frac"], gas_fraction)
def calc_sSFR(model, gals):
"""
Calculates the specific star formation rate (star formation divided by the stellar
mass of the galaxy) as a function of stellar mass.
The number of galaxies added to ``Model.properties["sSFR_mass"]`` and
``Model.properties["sSFR_sSFR"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size` weighted by ``number_gals_passed /``
:py:attr:`~sage_analysis.model.Model.num_gals_all_files`. If
this value is greater than ``number_gals_passed``, then all galaxies with non-zero
stellar mass will be used.
"""
non_zero_stellar = np.where(gals["StellarMass"][:] > 0.0)[0]
# Select a random subset of galaxies (if necessary).
random_inds = select_random_indices(non_zero_stellar, model.num_gals_all_files,
model.sample_size)
stellar_mass = np.log10(gals["StellarMass"][:][random_inds] * 1.0e10 / model.hubble_h)
sSFR = (gals["SfrDisk"][:][random_inds] + gals["SfrBulge"][:][random_inds]) / \
(gals["StellarMass"][:][random_inds] * 1.0e10 / model.hubble_h)
model.properties["sSFR_mass"] = np.append(model.properties["sSFR_mass"], stellar_mass)
model.properties["sSFR_sSFR"] = np.append(model.properties["sSFR_sSFR"], np.log10(sSFR))
def test_random_indices_less_than_number_available(
local_inds: np.ndarray,
global_num_inds_available: int,
global_num_inds_requested: int,
expected_inds: List[int],
):
"""
Parameterize Combinations
-------------------------
"""
seed = 666
inds = list(
select_random_indices(local_inds, global_num_inds_available,
global_num_inds_requested, seed))
assert inds == expected_inds
def calc_spatial(model, gals):
"""
Calculates the spatial position of the galaxies.
The number of galaxies added to ``Model.properties["<x/y/z>_pos"]`` arrays is given by
:py:attr:`~sage_analysis.model.Model.sample_size` weighted by
``number_galaxies_passed /`` :py:attr:`~sage_analysis.model.Model.num_gals_all_files`.
If this value is greater than ``number_galaxies_passed``, then all galaxies will be used.
"""
non_zero = np.where((gals["Mvir"][:] > 0.0) & (gals["StellarMass"][:] > 0.1))[0]
# Select a random subset of galaxies (if necessary).
non_zero = select_random_indices(non_zero, model.num_gals_all_files, model.sample_size)
attribute_names = ["x_pos", "y_pos", "z_pos"]
data_names = ["Posx", "Posy", "Posz"]
for (attribute_name, data_name) in zip(attribute_names, data_names):
# Units are Mpc/h.
pos = gals[data_name][:][non_zero]
model.properties[attribute_name] = np.append(model.properties[attribute_name], pos)