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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Victor Calderon
# Created : 2018-05-14
# Last Modified: 2019-03-06
from __future__ import absolute_import, division, print_function
__author__ = ['Victor Calderon']
__copyright__ = ["Copyright 2018 Victor Calderon"]
__email__ = ['victor.calderon@vanderbilt.edu']
__maintainer__ = ['Victor Calderon']
__all__ = [ 'Behroozi2010Relation',
'Moster2010Relation']
## Import modules
import numpy as np
import pandas as pd
from cosmo_utils.utils import file_utils as fd
from cosmo_utils.custom_exceptions import LSSUtils_Error
## Functions and Classes
# Behroozi Relation class
class Behroozi2010Relation(object):
"""
Class used to define the stellar-halo mass relation of
central galaxies as a function of halo mass.
"""
def __init__(self, **kwargs):
r"""
Parameters
-----------
input_h : {`int`, `float`} optional
Value of the Hubble constant in limits of between :math:`[0, 1]`.
This variable acts as the input `h` of variables, and it is set
to `1` by default.
out_h : {`int`, `float`} optional
Value of the Hubble constant in limits of between :math:`[0, 1]`.
This variable acts as the output `h` of variables, and it is set
to `1` by default.
"""
# Assigning variables
self.input_h = kwargs.get('input_h', 1)
self.out_h = kwargs.get('out_h', 1)
# Checking input parameters
self._check_input_parameters()
# Checking input parameters to make sure they are `expected`
def _check_input_parameters(self):
r"""
Checks whether or not the input parameters are what is expected or not.
"""
# Checking input types
h_type_arr = (float, int)
# `input_h` - Type
if not (isinstance(self.input_h, h_type_arr)):
msg = '`input_h` {0} is not a valid input type: {1}'
msg = msg.format(type(self.input_h), h_type_arr)
raise TypeError(msg)
# Checking input values
if not ((self.input_h > 0) and (self.input_h <= 1)):
msg = '`input_h` ({0}) is not within the proper range [0, 1]!'
msg = msg.format(self.input_h)
raise ValueError(msg)
# `out_h` - Type
if not (isinstance(self.out_h, h_type_arr)):
msg = '`out_h` {0} is not a valid input type: {1}'
msg = msg.format(type(self.out_h), h_type_arr)
raise TypeError(msg)
# Checking input values
if not ((self.out_h > 0) and (self.out_h <= 1)):
msg = '`out_h` ({0}) is not within the proper range [0, 1]!'
msg = msg.format(self.out_h)
raise ValueError(msg)
# Default dictionary to return to the user
def _return_default_dict(self):
r"""
Creates the directory used for the end-user with input variables of
the model.
Returns
-----------
param_dict : `dict`
Dictionary containing default model parameter values of
Behroozi et al. (2010) paper, plus any additional input
parameters to the model.
"""
param_dict = self._retrieve_model_default_dict()
param_dict['input_h'] = self.input_h
param_dict['out_h'] = self.out_h
return param_dict
# Default dictionary with input parameters
def _retrieve_model_default_dict(self):
r"""
Dictionary of the default values of all model parameters set to the
column 2 values in Table 2 of Behroozi et al. (2010) publication.
Returns
----------
d : `dict`
Dictionary containing default model parameter values of
Behroozi et al. (2010) paper.
Notes
---------
All calculations are done internally using the same h=0.7 units as
in Behroozi et al. (2010), ['arXiv:1001.0015'] so the parameter values
here are the same as in Table 2, even though the `mean_log_halo_mass`
and `mean_stellar_mass` methods use accept and return arguments
in ``h = 1`` units.
"""
# Main dictionary
d = ({'smhm_m0_0': 10.72,
'smhm_m0_a': 0.59,
'smhm_m1_0': 12.35,
'smhm_m1_a': 0.3,
'smhm_beta_0': 0.43,
'smhm_beta_a': 0.18,
'smhm_delta_0': 0.56,
'smhm_delta_a': 0.18,
'smhm_gamma_0': 1.54,
'smhm_gamma_a': 2.52})
return d
# Mean relation of the model to compute the mean halo mass of galaxies.
def mean_log_halo_mass(self, log_mstar, z=0):
r"""
Returns the halo mass of a central galaxy as a function of its stellar
mass.
Parameters
-----------
log_mstar : {`float`, `numpy.ndarray`, `list`}
Logarithmic 10-base stellar mass array of the galaxies.
z : {`float`, `int`}, optional
Redshift of the halo hosting the galaxy. If passing an array,
it must be the same length as the input `log_mstar`.
Returns
--------
mass_dict : `dict`
Dictionary with the logarithmic 10-base stellar mass and halo
mass arrays in the same units as `self.out_h`.
"""
# file_msg = fd.Program_Msg(__file__)
file_msg = '>>> '
little_h = 0.7
## Checking input parameters
# `log_mstar`
log_mstar_valid_types = (int, float, np.ndarray, list)
if not (isinstance(log_mstar, log_mstar_valid_types)):
msg = '{0} `log_mstar` ({1}) is not a valid input type: {2}!'
msg = msg.format(file_msg, type(log_mstar), log_mstar_valid_types)
raise TypeError(msg)
# `z` - Type
z_valid_types = (int, float)
if not (isinstance(z, z_valid_types)):
msg = '{0} `z` ({1}) is not a valid input type: {2}!'
msg = msg.format(file_msg, type(z), z_valid_types)
raise TypeError(msg)
# `z` - Value
if not (z >= 0):
msg = '{0} `z` ({1}) must be larger than `0`!'
msg = msg.format(file_msg, z)
raise ValueError(msg)
#
# Retrieving parameter dictionary
param_dict = self._retrieve_model_default_dict()
## Checking units of `log_mstar` and converting to ``h = 0.7`` if
## necessary.
# Converting stellar mass from units of `self.input_h` to `h == 1`
# and to `h = 0.7`
mstar_h1 = (10**log_mstar) * (self.input_h**2)
# Converting to `h=0.7` units
mstar = mstar_h1 / (little_h**2)
# Scale factor
a = 1./(1. + z)
# Behroozi function
logm0 = param_dict['smhm_m0_0'] + (param_dict['smhm_m0_a'] * (a - 1))
m0 = 10.**logm0
logm1 = param_dict['smhm_m1_0' ] + (param_dict['smhm_m1_a'] * (a - 1))
beta = param_dict['smhm_beta_0' ] + param_dict['smhm_beta_a' ]*(a - 1)
delta = param_dict['smhm_delta_0'] + param_dict['smhm_delta_a']*(a - 1)
gamma = param_dict['smhm_gamma_0'] + param_dict['smhm_gamma_a']*(a - 1)
#
stellar_mass_by_m0 = mstar / m0
term3_numerator = stellar_mass_by_m0**delta
term3_denominator = 1. + (stellar_mass_by_m0)**(-gamma)
#
# In units of `h = 0.7`
log_halo_mass = logm1 + (beta * np.log10(stellar_mass_by_m0))
log_halo_mass += (term3_numerator / term3_denominator) - 0.5
halo_mass = 10.**log_halo_mass
# Converting to desired units
log_mstar_key = np.log10(mstar * (little_h**2) / (self.out_h**2))
log_mhalo_key = np.log10(halo_mass * (little_h) / (self.out_h))
# Saving to dictionary
dict_names = ['log_mhalo', 'log_mstar']
dict_vars = [log_mhalo_key, log_mstar_key]
mass_dict = dict(zip(dict_names, dict_vars))
return mass_dict
def compute_example(self, log_mstar_min=8, log_mstar_max=12, mstep=500,
return_pd=True):
"""
Computes the ``Behroozi et al. (2010)`` relation for central galaxies.
Parameters
------------
log_mstar_min : {`float`, `int`}, optional
Logarithmic 10-base minimum stellar mass. This variable is set to
`10` by default.
log_mstar_max : {`float`, `int`}, optional
Logarithmic 10-base maximum stellar mass. This variable is set to
`15` by default.
mstep : {`float`, `int`} optional
Number of elements in the output arrays of halo masses and
stellar masses. This variable is set to `100` by default.
return_pd : `bool`, optional
If `True`, the function returns a `pandas.DataFrame` with the
logarithmic 10-base halo masses and stellar masses of central
galaxies in the units of `self.out_h`. This variable is set
to `True` by default.
Returns
---------
mass_obj : {`dict`, `pandas.DataFrame`}
Dictionary containing arrays or floats of the logarithmic
10-base halo mass and stellar masses of central galaxies in
units of `self.out_h`. If ``return_pd == True``, this function
returns a `pandas.DataFrame` instead.
"""
# Constants
redshift = 0.
# Defining logarithmic 10-base stellar mass array
log_stellar_arr = np.linspace(log_mstar_min, log_mstar_max, mstep)
# Computing dictionary of the Behroozi relation
mass_dict = self.mean_log_halo_mass(log_stellar_arr, z=redshift)
# Returning DataFrame if needed
if return_pd:
mass_obj = pd.DataFrame(mass_dict)
else:
mass_obj = mass_dict
return mass_obj
class Moster2010Relation(object):
"""
Class used to define the stellar-halo mass relation of
central galaxies as a function of halo mass.
"""
def __init__(self, **kwargs):
r"""
Parameters
-----------
input_h : {`int`, `float`} optional
Value of the Hubble constant in limits of between :math:`[0, 1]`.
This variable acts as the input `h` of variables, and it is set
to `1` by default.
out_h : {`int`, `float`} optional
Value of the Hubble constant in limits of between :math:`[0, 1]`.
This variable acts as the output `h` of variables, and it is set
to `1` by default.
"""
# Assigning variables
self.input_h = kwargs.get('input_h', 1)
self.out_h = kwargs.get('out_h', 1)
# Checking input parameters
self._check_input_parameters()
# Checking input parameters to make sure they are `expected`
def _check_input_parameters(self):
r"""
Checks whether or not the input parameters are what is expected or not.
"""
# Checking input types
h_type_arr = (float, int)
# `input_h` - Type
if not (isinstance(self.input_h, h_type_arr)):
msg = '`input_h` {0} is not a valid input type: {1}'
msg = msg.format(type(self.input_h), h_type_arr)
raise TypeError(msg)
# Checking input values
if not ((self.input_h > 0) and (self.input_h <= 1)):
msg = '`input_h` ({0}) is not within the proper range [0, 1]!'
msg = msg.format(self.input_h)
raise ValueError(msg)
# `out_h` - Type
if not (isinstance(self.out_h, h_type_arr)):
msg = '`out_h` {0} is not a valid input type: {1}'
msg = msg.format(type(self.out_h), h_type_arr)
raise TypeError(msg)
# Checking input values
if not ((self.out_h > 0) and (self.out_h <= 1)):
msg = '`out_h` ({0}) is not within the proper range [0, 1]!'
msg = msg.format(self.out_h)
raise ValueError(msg)
# Default dictionary to return to the user
def _return_default_dict(self):
r"""
Creates the directory used for the end-user with input variables of
the model.
Returns
-----------
param_dict : `dict`
Dictionary containing default model parameter values of
Moster et al. (2010) paper, plus any additional input
parameters to the model.
"""
param_dict = self._retrieve_model_default_dict()
param_dict['input_h'] = self.input_h
param_dict['out_h'] = self.out_h
return param_dict
# Default dictionary with input parameters
def _retrieve_model_default_dict(self):
r"""
Dictionary of the default values of all model parameters set to the
column 2 values in Table 2 of Moster et al. (2013) publication.
Returns
----------
d : `dict`
Dictionary containing default model parameter values of
Moster et al. (2010) paper.
Notes
---------
All calculations are done internally using the same h=0.7 units as
in Moster et al. (2010), ['arXiv:0903.4682'] so the parameter values
here are the same as in Table 2, even though the `mean_log_halo_mass`
and `mean_stellar_mass` methods use accept and return arguments
in ``h = 1`` units.
"""
# Main dictionary
d = ({'smhm_m1':11.899,
'smhm_m_M_0':0.02817,
'smhm_beta':1.068,
'smhm_gamma':0.611})
return d
# Mean relation of the model to compute the mean halo mass of galaxies.
def mean_log_stellar_mass(self, log_halo_mass, z=0):
r"""
Returns the halo mass of a central galaxy as a function of its halo
mass.
Parameters
-----------
log_halo_mass : {`float`, `numpy.ndarray`, `list`}
Logarithmic 10-base halo mass array of the galaxies.
z : {`float`, `int`}, optional
Redshift of the halo hosting the galaxy. If passing an array,
it must be the same length as the input `log_mstar`.
Returns
--------
mass_dict : `dict`
Dictionary with the logarithmic 10-base stellar mass and halo
mass arrays in the same units as `self.out_h`.
"""
# file_msg = fd.Program_Msg(__file__)
file_msg = '>>> '
little_h = 0.72
## Checking input parameters
# `log_halo_mass`
log_halo_mass_valid_types = (int, float, np.ndarray, list)
if not (isinstance(log_halo_mass, log_halo_mass_valid_types)):
msg = '{0} `log_halo_mass` ({1}) is not a valid input type: {2}!'
msg = msg.format(file_msg, type(log_halo_mass),
log_halo_mass_valid_types)
raise TypeError(msg)
# `z` - Type
z_valid_types = (int, float)
if not (isinstance(z, z_valid_types)):
msg = '{0} `z` ({1}) is not a valid input type: {2}!'
msg = msg.format(file_msg, type(z), z_valid_types)
raise TypeError(msg)
# `z` - Value
if not (z >= 0):
msg = '{0} `z` ({1}) must be larger than `0`!'
msg = msg.format(file_msg, z)
raise ValueError(msg)
#
# Retrieving parameter dictionary
param_dict = self._retrieve_model_default_dict()
## Checking units of `log_halo_mass` and converting to ``h = 0.7`` if
## necessary.
# Converting halo mass from units of `self.input_h` to `h == 1`
# and to `h = 0.7`
halo_mass_h1 = (10**log_halo_mass) * (self.input_h)
# Converting to `h=0.7` units
halo_mass = halo_mass_h1 / (little_h)
# Scale factor
a = 1./(1. + z)
# Moster 2010 function
halo_mass_by_m1 = halo_mass / (10.**param_dict['smhm_m1'])
m_m1_beta = halo_mass_by_m1**(-param_dict['smhm_beta'])
m_m1_gamma = halo_mass_by_m1**(param_dict['smhm_gamma'])
inverse_term = (m_m1_beta + m_m1_gamma)**(-1)
mass_over_M = 2. * param_dict['smhm_m_M_0'] * inverse_term
mstar = halo_mass * mass_over_M
# Converting to desired units
log_mstar_key = np.log10(mstar * (little_h**2) / (self.out_h**2))
log_mhalo_key = np.log10(halo_mass * (little_h) / (self.out_h))
# Saving to dictionary
dict_names = ['log_mhalo', 'log_mstar']
dict_vars = [log_mhalo_key, log_mstar_key]
mass_dict = dict(zip(dict_names, dict_vars))
return mass_dict
def compute_example(self, log_mhalo_min=10, log_halo_max=15, mstep=500,
return_pd=True):
"""
Computes the ``Moster et al. (2010)`` relation for central galaxies.
Parameters
------------
log_mhalo_min : {`float`, `int`}, optional
Logarithmic 10-base minimum halo mass. This variable is set to
`10` by default.
log_halo_max : {`float`, `int`}, optional
Logarithmic 10-base maximum halo mass. This variable is set to
`15` by default.
mstep : {`float`, `int`} optional
Number of elements in the output arrays of halo masses and
halo masses. This variable is set to `100` by default.
return_pd : `bool`, optional
If `True`, the function returns a `pandas.DataFrame` with the
logarithmic 10-base halo masses and stellar masses of central
galaxies in the units of `self.out_h`. This variable is set
to `True` by default.
Returns
---------
mass_obj : {`dict`, `pandas.DataFrame`}
Dictionary containing arrays or floats of the logarithmic
10-base halo mass and stellar masses of central galaxies in
units of `self.out_h`. If ``return_pd == True``, this function
returns a `pandas.DataFrame` instead.
"""
# Constants
redshift = 0.
# Defining logarithmic 10-base stellar mass array
log_mhalo_arr = np.linspace(log_mhalo_min, log_halo_max, mstep)
# Computing dictionary of the Behroozi relation
mass_dict = self.mean_log_stellar_mass(log_mhalo_arr, z=redshift)
# Returning DataFrame if needed
if return_pd:
mass_obj = pd.DataFrame(mass_dict)
else:
mass_obj = mass_dict
return mass_obj
## Retrieves default values for Behroozi et al. (2013)
def _retrieve_Behroozi_default_dict():
"""
Dictionary of default values of all model parameters set to the
column 2 values in Table 2 of Behroozi et al. (2013)
Returns
--------
d : `dict`
Dictionary containing default parameters for the Stellar-Halo
Mass relation of Behroozi et al. (2013)
Notes
----------
All calculations are done internally ising the same h=0.7 units as
in Behroozi ete al. (2010), ['arXiv:1001.0015'] so the parameter values
here are the same as in Table 2, even though the `mean_log_halo_mass`
and `mean_stellar_mass` methods use accept and return arguments in
h=1 units.
"""
## Main dictionary
d = ({'smhm_m0_0': 10.72,
'smhm_m0_a': 0.59,
'smhm_m1_0': 12.35,
'smhm_m1_a': 0.3,
'smhm_beta_0': 0.43,
'smhm_beta_a': 0.18,
'smhm_delta_0': 0.56,
'smhm_delta_a': 0.18,
'smhm_gamma_0': 1.54,
'smhm_gamma_a': 2.52})
return d
## Behroozi SHMR function
def Behroozi_relation(log_mstar, z=0.):
"""
Returns the halo mass of a central galaxy as a function of its stellar
mass.
Parameters
-----------
log_mstar : `float` ,`np.ndarray`, or array-like
Value or array of values of base-10 logarithm of stellar mass
in h=1 solar mass units.
z : int, float, `np.ndarray` or array-like
Redshift of the halo hosting the galaxy. If passing an array,
it must be of the same length as the input `log_mstar`.
Returns
-----------
log_halo_mass : float or `np.ndarray`
Array or float containing 10-base logarithm of halo mass in ``h=1``
solar mass units.
Notes
----------
The parameter values in Behroozi+10 were fit to data assuming ``h=0.7``.
Thus, we will transform our input stellar mass to ``h=0.7`` units,
evaluate using the Behroozi parameters, and then transform back to
``h=1`` units before returning the result.
"""
file_msg = fd.Program_Msg(__file__)
little_h = 0.7
## Checking input parameters
# `log_mstar`
mstar_valid_types = (int, float, np.ndarray, list)
if not (isinstance(log_mstar, mstar_valid_types)):
msg = '{0} `log_mstar` ({1}) is not a valid type!'.format(
file_msg, type(log_mstar))
raise TypeError(msg)
##
## Behroozi dictionary
param_dict = _retrieve_Behroozi_default_dict()
## COnverting stellar mass from ``h=1`` units to ``h=0.7`` units.
mstar = (10.**log_mstar) / (little_h**2)
## Scale factor
a = 1./(1. + z)
##
## Behroozi function
logm0 = param_dict['smhm_m0_0'] + param_dict['smhm_m0_a']*(a - 1.)
m0 = 10.**logm0
logm1 = param_dict['smhm_m1_0' ] + param_dict['smhm_m1_a' ]*(a - 1)
beta = param_dict['smhm_beta_0' ] + param_dict['smhm_beta_a' ]*(a - 1)
delta = param_dict['smhm_delta_0'] + param_dict['smhm_delta_a']*(a - 1)
gamma = param_dict['smhm_gamma_0'] + param_dict['smhm_gamma_a']*(a - 1)
#
stellar_mass_by_m0 = mstar/m0
term3_numerator = (stellar_mass_by_m0)**delta
term3_denominator = 1. + (stellar_mass_by_m0)**(-gamma)
#
log_halo_mass = logm1 + beta*np.log10(stellar_mass_by_m0)
log_halo_mass += (term3_numerator/term3_denominator) - 0.5
#
# Convert back from ``h=0.7`` to ``h=1`` units
return np.log10((10.**log_halo_mass)*(little_h))
## Retrieves default values for Moster et al. (2010)
def _retrieve_Moster_default_dict():
"""
Dictionary of default values of all model parameters set to the
column 2 values in Table 2 of Moster et al. (2010)
Returns
--------
d : `dict`
Dictionary containing default parameters for the Stellar-Halo
Mass relation of Moster et al. (2010)
Notes
----------
All calculations are done internally using the same h=0.7 units as
in Moster et al. (2010), ['arXiv:0903.4682'] so the parameter values
here are the same as in Table 2, even though the `mean_log_halo_mass`
and `mean_stellar_mass` methods use accept and return arguments in
h=1 units.
"""
## Main dictionary
d = ({'smhm_m1':11.899,
'smhm_m_M_0':0.02817,
'smhm_beta':1.068,
'smhm_gamma':0.611})
return d
## Moster SHMR function
def Moster2010_relation(log_halo_mass, return_h0=False):
"""
Returns the halo mass of a central galaxy as a function of its stellar
mass.
Parameters
-----------
log_halo_mass : `float` ,`np.ndarray`, or array-like
Value or array of values of base-10 logarithm of halo mass
in ``h = 1`` solar mass units.
return_h0 : `bool`, optional
If `True`, the function returns both stellar mass and halo mass
arrays in units of ``h = 1`` units.
Returns
-----------
mass_dict : `dict`
Dictionary containing arrays or floats containing 10-base logarithm
of halo mass and stellar mass of central galaxies in units of
either ``h = 1`` or ``h = 0.7``. The units of ``h`` depend on the
choice of ``return_h0``.
Notes
----------
The parameter values in Moster+10 were fit to data assuming ``h=0.7``.
Thus, we will transform our input stellar mass to ``h=0.7`` units,
evaluate using the Moster parameters, and then transform back to
``h=1`` units before returning the result.
"""
# file_msg = fd.Program_Msg(__file__)
file_msg = '>>>'
little_h = 0.72
## Checking input parameters
# `log_halo_mass`
mstar_valid_types = (int, float, np.ndarray, list)
if not (isinstance(log_halo_mass, mstar_valid_types)):
msg = '{0} `log_halo_mass` ({1}) is not a valid type!'.format(
file_msg, type(log_halo_mass))
raise LSSUtils_Error(msg)
##
## Moster dictionary
param_dict = _retrieve_Moster_default_dict()
# Converting halo mass from h=1 to h=0.7
log_halo_mass -= np.log10(little_h)
halo_mass = (10.**log_halo_mass)
##
## Moster function
mass_over_m1 = halo_mass / (10.**param_dict['smhm_m1'])
m_m1_beta = mass_over_m1**(-1. * param_dict['smhm_beta'])
m_m1_gamma = mass_over_m1**(param_dict['smhm_gamma'])
inverse_term = (m_m1_beta + m_m1_gamma)**(-1)
mass_over_M = 2. * param_dict['smhm_m_M_0'] * inverse_term
stellar_mass = halo_mass * mass_over_M
# Converting to log-scale
log_stellar_mass = np.log10(stellar_mass)
# Choosing which values to return
if (return_h0):
# Returns both masses in units of ``h = 1``
log_stellar_mass += np.log10(little_h**2)
log_halo_mass += np.log10(little_h)
# Dictionary of masses
return_obj = [log_halo_mass, log_stellar_mass]
mass_dict = dict(zip(['m_halo', 'm_stellar'], return_obj))
return mass_dict
def Moster_example(logmhalo_min=11, logmhalo_max=15, return_h0=True):
"""
Computes the ``Moster et al. (2010)`` relation and returns a dictionary
of stellar masses and halo masses of central galaxies
Parameters
------------
return_h0 : `bool`, optional
If `True`, the function returns both stellar mass and halo mass
arrays in units of ``h = 1`` units.
Returns
------------
mass_dict : `dict`
Dictionary containing arrays or floats containing 10-base logarithm
of halo mass and stellar mass of central galaxies in units of
either ``h = 1`` or ``h = 0.7``. The units of ``h`` depend on the
choice of ``return_h0``.
"""
# file_msg = fd.Program_Msg(__file__)
file_msg = '>> '
# Constants
dlogM = 1000
# Checking input parameters
# `logmhalo_min`
mhalo_min_max_type = (float, int)
if not (isinstance(logmhalo_min, mhalo_min_max_type)):
msg = '{0} `logmhalo_min` ({1}) is not a valid input type: {2}'
msg = msg.format(file_msg, type(logmhalo_min), mhalo_min_max_type)
raise TypeError(msg)
else:
logmhalo_min = float(logmhalo_min)
# `logmhalo_max`
if not (isinstance(logmhalo_max, mhalo_min_max_type)):
msg = '{0} `logmhalo_max` ({1}) is not a valid input type: {2}'
msg = msg.format(file_msg, type(logmhalo_max), mhalo_min_max_type)
raise TypeError(msg)
else:
logmhalo_max = float(logmhalo_max)
# Checking relation between `logmhalo_min` and `mhalo_max`
if not (logmhalo_min < logmhalo_max):
msg = '{0} `logmhalo_min` ({1}) must be smaller than `logmhalo_max` '
msg = '({2})!'
msg = msg.format(file_msg, logmhalo_min, logmhalo_max)
raise ValueError(msg)
# Creating array of halo masses in units of ``h = 1``
log_halo_mass_arr = np.linspace(logmhalo_min, logmhalo_max, dlogM)
# Computing stellar masses
mass_dict = Moster2010_relation(log_halo_mass_arr, return_h0=return_h0)
return mass_dict
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