def __init__(
self,
cosmo_model='Planck15',
model_type='LF',
model_name='SchCut',
model_par={
'phistar': 8.7e-11 * u.Lsun**-1 * u.Mpc**-3,
'Lstar': 2.1e6 * u.Lsun,
'alpha': -1.87,
'Lmin': 500 * u.Lsun
},
nu=115 * u.GHz,
nuObs=30 * u.GHz,
Mmin=1e9 * u.Msun,
Mmax=1e15 * u.Msun,
nM=5000,
hmf_model='Tinker08',
Lmin=100 * u.Lsun,
Lmax=1e8 * u.Lsun,
nL=5000,
kmin=1e-2 * u.Mpc**-1,
kmax=10. * u.Mpc**-1,
nk=100,
sigma_scatter=0.,
fduty=1.,
do_onehalo=False,
do_Jysr=False):
# Get list of input values to check type and units
self._lim_params = locals()
self._lim_params.pop('self')
# Get list of input names and default values
self._default_lim_params = get_default_params(LineModel.__init__)
# Check that input values have the correct type and units
check_params(self._lim_params, self._default_lim_params)
# Set all given parameters
for key in self._lim_params:
setattr(self, key, self._lim_params[key])
# Create overall lists of parameters (Only used if using one of
# lim's subclasses
self._input_params = {} # Don't want .update to change _lim_params
self._default_params = {}
self._input_params.update(self._lim_params)
self._default_params.update(self._default_lim_params)
# Create list of cached properties
self._update_list = []
# Check if model_name is valid
check_model(self.model_type, self.model_name)
def update(self, **new_params):
# Check if params dict contains valid parameters
check_params(new_params, self._default_params)
# If model_type or model_name is updated, check if model_name is valid
if ('model_type' in new_params) and ('model_name' in new_params):
check_model(new_params['model_type'], new_params['model_name'])
elif 'model_type' in new_params:
check_model(new_params['model_type'], self.model_name)
elif 'model_name' in new_params:
check_model(self.model_type, new_params['model_name'])
# Clear cached properties so they can be updated
for attribute in self._update_list:
# Use built-in hmf updater to change h
if attribute != 'h':
delattr(self, attribute)
self._update_list = []
# Set new parameter values
for key in new_params:
setattr(self, key, new_params[key])
self._current_params[key] = new_params[key]
# Update hmf if cosmological model has changed
if 'cosmo_model' in new_params:
self.h.update(cosmo_model=new_params['cosmo_model'])
if 'hmf_model' in new_params:
self.h.update(hmf_model=new_params['hmf_model'])
if 'nuObs' in new_params:
self.h.update(z=self.z)
if 'nu' in new_params:
self.h.update(z=self.z)
def __init__(
self,
cosmo_input=dict(f_NL=0,
H0=67.0,
cosmomc_theta=None,
ombh2=0.022,
omch2=0.12,
omk=0.0,
neutrino_hierarchy='degenerate',
num_massive_neutrinos=1,
mnu=0.06,
nnu=3.046,
YHe=None,
meffsterile=0.0,
standard_neutrino_neff=3.046,
TCMB=2.7255,
tau=None,
deltazrei=None,
bbn_predictor=None,
theta_H0_range=[10, 100],
w=-1.0,
wa=0.,
cs2=1.0,
dark_energy_model='ppf',
As=2e-09,
ns=0.96,
nrun=0,
nrunrun=0.0,
r=0.0,
nt=None,
ntrun=0.0,
pivot_scalar=0.05,
pivot_tensor=0.05,
parameterization=2,
halofit_version='mead'),
model_type='LF',
model_name='SchCut',
model_par={
'phistar': 9.6e-11 * u.Lsun**-1 * u.Mpc**-3,
'Lstar': 2.1e6 * u.Lsun,
'alpha': -1.87,
'Lmin': 5000 * u.Lsun
},
hmf_model='ST',
bias_model='ST99',
bias_par={}, #Otherwise, write a dict with the corresponding values
nu=115 * u.GHz,
nuObs=30 * u.GHz,
Mmin=1e9 * u.Msun,
Mmax=1e15 * u.Msun,
nM=5000,
Lmin=100 * u.Lsun,
Lmax=1e8 * u.Lsun,
nL=5000,
kmin=1e-2 * u.Mpc**-1,
kmax=10. * u.Mpc**-1,
nk=100,
k_kind='log',
sigma_scatter=0.,
fduty=1.,
do_onehalo=False,
do_Jysr=False,
do_RSD=True,
sigma_NL=7 * u.Mpc,
nmu=1000,
nonlinear_pm=False,
FoG_damp='Lorentzian',
smooth=False):
# Get list of input values to check type and units
self._lim_params = locals()
self._lim_params.pop('self')
# Get list of input names and default values
self._default_lim_params = get_default_params(LineModel.__init__)
# Check that input values have the correct type and units
check_params(self._lim_params, self._default_lim_params)
# Set all given parameters
for key in self._lim_params:
setattr(self, key, self._lim_params[key])
# Create overall lists of parameters (Only used if using one of
# lim's subclasses
self._input_params = {} # Don't want .update to change _lim_params
self._default_params = {}
self._input_params.update(self._lim_params)
self._default_params.update(self._default_lim_params)
# Create list of cached properties
self._update_list = []
# Check if model_name is valid
check_model(self.model_type, self.model_name)
check_bias_model(self.bias_model)
#Set cosmology and call camb
self.cosmo_input = self._default_params['cosmo_input']
for key in cosmo_input:
self.cosmo_input[key] = cosmo_input[key]
self.camb_pars = camb.set_params(
H0=self.cosmo_input['H0'],
cosmomc_theta=self.cosmo_input['cosmomc_theta'],
ombh2=self.cosmo_input['ombh2'],
omch2=self.cosmo_input['omch2'],
omk=self.cosmo_input['omk'],
neutrino_hierarchy=self.cosmo_input['neutrino_hierarchy'],
num_massive_neutrinos=self.cosmo_input['num_massive_neutrinos'],
mnu=self.cosmo_input['mnu'],
nnu=self.cosmo_input['nnu'],
YHe=self.cosmo_input['YHe'],
meffsterile=self.cosmo_input['meffsterile'],
standard_neutrino_neff=self.cosmo_input['standard_neutrino_neff'],
TCMB=self.cosmo_input['TCMB'],
tau=self.cosmo_input['tau'],
deltazrei=self.cosmo_input['deltazrei'],
bbn_predictor=self.cosmo_input['bbn_predictor'],
theta_H0_range=self.cosmo_input['theta_H0_range'],
w=self.cosmo_input['w'],
cs2=self.cosmo_input['cs2'],
dark_energy_model=self.cosmo_input['dark_energy_model'],
As=self.cosmo_input['As'],
ns=self.cosmo_input['ns'],
nrun=self.cosmo_input['nrun'],
nrunrun=self.cosmo_input['nrunrun'],
r=self.cosmo_input['r'],
nt=self.cosmo_input['nt'],
ntrun=self.cosmo_input['ntrun'],
pivot_scalar=self.cosmo_input['pivot_scalar'],
pivot_tensor=self.cosmo_input['pivot_tensor'],
parameterization=self.cosmo_input['parameterization'],
halofit_version=self.cosmo_input['halofit_version'])
self.camb_pars.WantTransfer = True
def update(self, **new_params):
# Check if params dict contains valid parameters
#check_params(new_params,self._default_params)
# If model_type or model_name is updated, check if model_name is valid
if ('model_type' in new_params) and ('model_name' in new_params):
check_model(new_params['model_type'], new_params['model_name'])
elif 'model_type' in new_params:
check_model(new_params['model_type'], self.model_name)
elif 'model_name' in new_params:
check_model(self.model_type, new_params['model_name'])
# Clear cached properties so they can be updated
for attribute in self._update_list:
# Use built-in hmf updater to change h
if attribute != 'cosmo_input':
delattr(self, attribute)
self._update_list = []
# Set new parameter values
for key in new_params:
if key != 'cosmo_input':
setattr(self, key, new_params[key])
if 'cosmo_input' in new_params:
temp = new_params['cosmo_input']
for key in temp:
self.cosmo_input[key] = temp[key]
self.camb_pars = camb.set_params(
H0=self.cosmo_input['H0'],
cosmomc_theta=self.cosmo_input['cosmomc_theta'],
ombh2=self.cosmo_input['ombh2'],
omch2=self.cosmo_input['omch2'],
omk=self.cosmo_input['omk'],
neutrino_hierarchy=self.cosmo_input['neutrino_hierarchy'],
num_massive_neutrinos=self.
cosmo_input['num_massive_neutrinos'],
mnu=self.cosmo_input['mnu'],
nnu=self.cosmo_input['nnu'],
YHe=self.cosmo_input['YHe'],
meffsterile=self.cosmo_input['meffsterile'],
standard_neutrino_neff=self.
cosmo_input['standard_neutrino_neff'],
TCMB=self.cosmo_input['TCMB'],
tau=self.cosmo_input['tau'],
deltazrei=self.cosmo_input['deltazrei'],
bbn_predictor=self.cosmo_input['bbn_predictor'],
theta_H0_range=self.cosmo_input['theta_H0_range'],
w=self.cosmo_input['w'],
wa=self.cosmo_input['wa'],
cs2=self.cosmo_input['cs2'],
dark_energy_model=self.cosmo_input['dark_energy_model'],
As=self.cosmo_input['As'],
ns=self.cosmo_input['ns'],
nrun=self.cosmo_input['nrun'],
nrunrun=self.cosmo_input['nrunrun'],
r=self.cosmo_input['r'],
nt=self.cosmo_input['nt'],
ntrun=self.cosmo_input['ntrun'],
pivot_scalar=self.cosmo_input['pivot_scalar'],
pivot_tensor=self.cosmo_input['pivot_tensor'],
parameterization=self.cosmo_input['parameterization'],
halofit_version=self.cosmo_input['halofit_version'])