# Host
import numpy as np
# =============== #
# #
# Mass & SFR #
# #
# =============== #
[docs]
def get_sfr_as_function_of_mass_and_redshift(mass, redshift):
"""Get the star formation rate as a function of mass and redshift.
This is based on equation A.7 from Childress et al. 2014, used in
Wiseman et al. 2021.
Parameters
----------
mass : float or array
The mass of the host galaxy.
redshift : float or array
The redshift of the host galaxy.
Returns
-------
float or array
The star formation rate.
"""
ampl = (10**(mass)/10**10)**0.7
fraction = np.exp(1.9*redshift)/ (np.exp(1.7* (redshift-2)) + np.exp(0.2* (redshift-2)))
return ampl * fraction
[docs]
def get_schechterpdf(mass, mstar, alpha, phi, alpha2=None, phi2=None):
"""Get the Schechter probability density function.
Parameters
----------
mass : float or array
Stellar mass value(s) at which to evaluate the Schechter function.
mstar : float or array
Characteristic stellar mass defining the exponential cutoff at higher masses.
alpha : float
Low-mass end slope of the Schechter function.
phi : float
Normalization of the Schechter function.
alpha2 : None, float, optional
Second low-mass end slope (for double Schechter).
By default None.
phi2 : None, float, optional
Second normalization of the Schechter function (for double Schechter).
By default None.
Returns
-------
float or array
The single or double Schechter pdf.
"""
delta_logmass = mass-mstar
# single schechter
if alpha2 is None or phi2 is None:
return np.log(10)* np.exp(-10**(delta_logmass)) * phi*(10**delta_logmass)**(1+alpha)
# double schechter
return np.log(10)* np.exp(-10**(delta_logmass)) * 10**(delta_logmass) * (phi*10**(delta_logmass*alpha) +
phi2*10**(delta_logmass*alpha2))
[docs]
def get_stellarmassfunction(redshift, which="all", xx="6:13:100j"):
"""Get the stellar mass function.
Parameters
----------
redshift : float or array
The redshift of the host galaxy.
which : str, optional
Which stellar mass function to use. Can be "all", "blue", or "red".
The default is "all".
xx : str, optional
The mass range to use. The default is "6:13:100j".
Returns
-------
tuple
A tuple containing the mass array and the pdf.
"""
if isinstance(xx, str): # assumed r_ input
xx = eval(f"np.r_[{xx}]")
prop = {#(0, 0.3): # Driver et al. 2022
#{"blue":{"mstar": 10.70, "phi":0.855*1e-3, "alpha":-1.39}, # Disc | Moffett2016
# "red": {"mstar": 10.74, "phi":3.67*1e-3, "alpha":-0.525}, # Spherical | Moffett2016
# "all": {"mstar": 10.745, "phi":10**(-2.437), "alpha":-0.466, "phi2":10**(-3.201), "alpha2":-1.530}, # Driver et al. 2022
#},
(0., 0.5): # Mortlock et al. 2015
{"blue":{"mstar": 10.83, "phi":10**(-3.31), "alpha":-1.41},
"red": {"mstar": 10.90, "phi":10**(-4.87), "alpha":-1.74, "phi2":10**(-2.80), "alpha2":-0.42},
"all": {"mstar": 10.90, "phi":10**(-3.51), "alpha":-1.59, "phi2":10**(-2.59), "alpha2":-0.71},
},
(0.5, 1.0):# Mortlock et al. 2015
{"blue":{"mstar": 10.77, "phi":10**(-3.28), "alpha":-1.45},
"red": {"mstar": 10.77, "phi":10**(-4.11), "alpha":-1.37, "phi2":10**(-2.75), "alpha2":-0.27},
"all": {"mstar": 10.90, "phi":10**(-3.21), "alpha":-1.42, "phi2":10**(-2.93), "alpha2":-0.49},
},
(1.0, 1.5):# Mortlock et al. 2015
{"blue":{"mstar": 10.64, "phi":10**(-3.14), "alpha":-1.37},
"red": {"mstar": 10.78, "phi":10**(-2.96), "alpha":-0.35},
"all": {"mstar": 11.04, "phi":10**(-3.21), "alpha":-1.31},
},
(1.5, 2.0):# Mortlock et al. 2015
{"blue":{"mstar": 11.01, "phi":10**(-4.05), "alpha":-1.74},
"red": {"mstar": 10.71, "phi":10**(-3.31), "alpha":-0.24},
"all": {"mstar": 11.00, "phi":10**(-3.50), "alpha":-1.40}, # guess, missing line
}
}
redshift = np.atleast_1d(redshift)
if len(redshift)==1:
redshift_ = redshift[0]
for k,prop_ in prop.items():
if redshift_>k[0] and redshift_<k[1]:
break
pdf = [get_schechterpdf(xx, **prop_[which])]
else:
pdf = []
for redshift_ in redshift:
for k,prop_ in prop.items():
if redshift_>k[0] and redshift_<k[1]:
break
pdf.append(get_schechterpdf(xx, **prop_[which]))
return xx, np.asarray(pdf)
