def setUp(self):
"""
set up data used in the tests.
setUp is called before each test function execution.
"""
self.cosmo = Class()
self.cosmo_newt = Class()
if CLASS_VERBOSE:
self.verbose = {
'input_verbose': 1,
'background_verbose': 1,
'thermodynamics_verbose': 1,
'perturbations_verbose': 1,
'transfer_verbose': 1,
'primordial_verbose': 1,
'harmonic_verbose': 1,
'fourier_verbose': 1,
'lensing_verbose': 1,
'distortions_verbose': 1,
'output_verbose': 1,
}
else:
self.verbose = {}
self.scenario = {}
def main(target=args['target'], base=args['base'], new=args['new']):
# create instance of the class "Class"
TargetCosmo = Class()
# pass input parameters
print("The default cosmology is read from " + target)
target_param_dict = read_file(target)
TargetCosmo.set(target_param_dict)
# run class
TargetCosmo.compute()
os.remove(target_param_dict[rootName] + "parameters.ini")
os.remove(target_param_dict[rootName] + "unused_parameters")
theta_target = TargetCosmo.theta_s_100()
print("Target 100*theta_s = ", theta_target)
# The second (new) cosmology
print("The new cosmology is read from " + base + " and " + new[1])
base_param_dict = read_file(base)
# Create a new table with the final cosmologies
if new[0] == 'table':
shutil.copy(new[1], cosmology_table)
new_params, numCosm = read_input(new)
# for each new cosmology
for iCosm in range(numCosm):
# Check whether the hubble is set to the TBD value (HubbleDef); if not, don't meddle
if np.abs(new_params[HubbleParam][iCosm] -
HubbleDef) > 1.e-7 and new[0] == 'table':
continue
NewCosmo = Class()
# Load the base parameter values
NewCosmo.set(base_param_dict)
# Create a dictionary
new_param_dict = read_line(new_params,
iCosm) if new[0] == 'table' else new_params
if new_param_dict[rootName][-1:] != '.':
new_param_dict[rootName] += '.'
# create new directory with the root name unless it exists already
dir_par = new_param_dict[rootName][:-1]
if os.path.isdir(dir_par) != True: os.mkdir(dir_par)
os.chdir(dir_par)
NewCosmo.set(new_param_dict)
# run class
NewCosmo.compute()
h = search(NewCosmo, theta_target)
write_dict_to_ini(new_param_dict, h)
os.chdir('..')
# if running in table regime, modify the README table and delete everything else
if new[0] == 'table':
# modify the H0 and A_s columns in the final table
A_s = new_param_dict['A_s']
modify_table(cosmology_table, new_param_dict[rootName][:-1], h,
A_s)
# Get rid of the evidence
shutil.rmtree(dir_par)
def __init__(self, cosmology=None):
'''
This is an interface I made to talk to the cython
module for class.
The inputs are a dictionary containing cosmological parameters.
If the input cosmology is not given, then the default is Planck 2015.
'''
if (cosmology == None):
# default to planck cosmology 2015
params = {
'output': 'tCl lCl,mPk, mTk',
'l_max_scalars': 2000,
'A_s': 2.3e-9,
'n_s': 0.9624,
'h': 0.6774,
'omega_b': 0.02230,
'omega_cdm': 0.1188,
'k_pivot': 0.05,
'A_s': 2.142e-9,
'n_s': 0.9667,
'P_k_max_1/Mpc': 500.0,
'N_eff': 3.04,
'Omega_fld': 0,
'YHe': 0.2453,
'z_reio': 8.8
}
else:
params = cosmology
self.k_max = params['P_k_max_1/Mpc']
self.cosmo = Class()
self.cosmo.set(params)
self.cosmo.compute()
params = {
'output': 'tCl lCl,mPk, mTk',
'l_max_scalars': 2000,
'z_pk': 0,
'A_s': 2.3e-9,
'n_s': 0.9624,
'h': 0.6774,
'omega_b': 0.02230,
'omega_cdm': 0.1188,
'k_pivot': 0.05,
'A_s': 2.142e-9,
'n_s': 0.9667,
'P_k_max_1/Mpc': 500.0,
'N_eff': 3.04,
'Omega_fld': 0,
'YHe': 0.2453,
'z_reio': 8.8,
'non linear': 'halofit'
}
self.non_linear_cosmo = Class()
self.non_linear_cosmo.set(params)
self.non_linear_cosmo.compute()
def class_pk(self, z, cosmo_params=None, pk_params=None, return_s8=False):
if cosmo_params is None:
cosmo_params = self.cosmo_params
if pk_params is None:
pk_params = self.pk_params
cosmoC = Class()
h = cosmo_params['h']
class_params = {
'h': h,
'omega_b': cosmo_params['Omb'] * h**2,
'omega_cdm': (cosmo_params['Om'] - cosmo_params['Omb']) * h**2,
'A_s': cosmo_params['Ase9'] * 1.e-9,
'n_s': cosmo_params['ns'],
'output': 'mPk',
'z_max_pk': 100, #max(z)*2, #to avoid class error.
#Allegedly a compiler error, whatever that means
'P_k_max_1/Mpc': pk_params['kmax'] * h * 1.1,
}
if pk_params['non_linear'] == 1:
class_params['non linear'] = 'halofit'
class_params['N_ur'] = 3.04 #ultra relativistic species... neutrinos
if cosmo_params['mnu'] != 0:
class_params['N_ur'] -= 1 #one massive neutrino
class_params['m_ncdm'] = cosmo_params['mnu']
class_params['N_ncdm'] = 3.04 - class_params['N_ur']
if cosmo_params['w'] != -1 or cosmo_params['wa'] != 0:
class_params['Omega_fld'] = cosmo_params['Oml']
class_params['w0_fld'] = cosmo_params['w']
class_params['wa_fld'] = cosmo_params['wa']
for ke in class_accuracy_settings.keys():
class_params[ke] = class_accuracy_settings[ke]
cosmoC = Class()
cosmoC.set(class_params)
try:
cosmoC.compute()
except Exception as err:
print(class_params, err)
raise Exception('Class crashed')
k = self.kh * h
pkC = np.array([[cosmoC.pk(ki, zj) for ki in k] for zj in z])
pkC *= h**3
s8 = cosmoC.sigma8()
cosmoC.struct_cleanup()
if return_s8:
return pkC, self.kh, s8
else:
return pkC, self.kh
def recover_cosmological_module(data):
"""
From the cosmological module name, initialise the proper Boltzmann code
.. note::
Only CLASS is currently wrapped, but a python wrapper of CosmoMC should
enter here.
"""
# Importing the python-wrapped CLASS from the correct folder, defined in
# the .conf file, or overwritten at this point by the log.param.
# If the cosmological code is CLASS, do the following to import all
# relevant quantities
if data.cosmological_module_name == 'CLASS':
try:
classy_path = ''
for elem in os.listdir(os.path.join(
data.path['cosmo'], "python", "build")):
if elem.find("lib.") != -1:
classy_path = os.path.join(
data.path['cosmo'], "python", "build", elem)
break
except OSError:
raise io_mp.ConfigurationError(
"You probably did not compile the python wrapper of CLASS. " +
"Please go to /path/to/class/python/ and do\n" +
"..]$ python setup.py build")
# Inserting the previously found path into the list of folders to
# search for python modules.
sys.path.insert(1, classy_path)
try:
from classy import Class
except ImportError:
raise io_mp.MissingLibraryError(
"You must have compiled the classy.pyx file. Please go to " +
"/path/to/class/python and run the command\n " +
"python setup.py build")
# FK: we need two independent instances of Class!
cosmo1 = Class()
cosmo2 = Class()
else:
raise io_mp.ConfigurationError(
"Unrecognised cosmological module. " +
"Be sure to define the correct behaviour in MontePython.py " +
"and data.py, to support a new one.")
return cosmo1, cosmo2
def class_pk(self, z, cosmo_params=None, pk_params=None, return_s8=False):
#output is in same unit as CAMB
if not cosmo_params:
cosmo_params = self.cosmo_params
if not pk_params:
pk_params = self.pk_params
kh = np.logspace(np.log10(pk_params['kmin']),
np.log10(pk_params['kmax']), pk_params['nk'])
cosmoC = Class()
h = cosmo_params['h']
class_params = {
'h': h,
'omega_b': cosmo_params['Omb'] * h**2,
'omega_cdm': (cosmo_params['Om'] - cosmo_params['Omb']) * h**2,
'A_s': cosmo_params['As'],
'n_s': cosmo_params['ns'],
'output': 'mPk',
'z_max_pk': max(z) + 0.1,
'P_k_max_1/Mpc': pk_params['kmax'] * h,
}
if pk_params['non_linear'] == 1:
class_params['non linear'] = 'halofit'
class_params['N_ur'] = 3.04 #ultra relativistic species... neutrinos
if cosmo_params['mnu'] != 0:
class_params['N_ur'] -= 1 #one massive neutrino
class_params['m_ncdm'] = cosmo_params['mnu']
class_params['N_ncdm'] = 3.04 - class_params['N_ur']
if cosmo_params['w'] != -1:
class_params['Omega_fld'] = cosmo_params['Oml']
class_params['w0_fld'] = cosmo_params['w']
class_params['wa_fld'] = cosmo_params['wa']
for ke in class_accuracy_settings.keys():
class_params[ke] = class_accuracy_settings[ke]
cosmoC = Class()
cosmoC.set(class_params)
cosmoC.compute()
k = kh * h #output is in same unit as CAMB
pkC = np.array([[cosmoC.pk(ki, zj) for ki in k] for zj in z])
pkC *= h**3
s8 = cosmoC.sigma8()
cosmoC.struct_cleanup()
if return_s8:
return pkC, kh, s8
else:
return pkC, kh
def compute_power_spectra(self, z):
try:
from classy import Class
except ImportError:
print("Cannot precompute power spectra because CLASS "+\
"is not installed.")
return
cos = self.args['cosmology']
h = cos['h']
params = {
'output': 'mPk',
"h": h,
"sigma8": cos['sigma8'],
"n_s": cos['ns'],
"Omega_b": cos['Omega_b'],
"Omega_cdm": cos['Omega_m'] - cos['Omega_b'],
'P_k_max_1/Mpc': 1000.,
'z_max_pk': 1.0,
'non linear': 'halofit'
}
class_cosmo = Class()
class_cosmo.set(params)
class_cosmo.compute()
k = np.logspace(-5, 3, num=4000) #1/Mpc comoving
kh = k / h #h/Mpc comoving
#P(k) are in Mpc^3/h^3 comoving
Pnl = np.array([class_cosmo.pk(ki, z) for ki in k]) * h**3
Plin = np.array([class_cosmo.pk_lin(ki, z) for ki in k]) * h**3
self.args['k'] = kh
self.args['Plin'] = Plin
self.args['Pnl'] = Pnl
return
def clkk_gen(Omega_m, A_se9, zs=1.0):
A_s = A_se9 * 1e-9
omch2 = (OmegaM - OmegaB) * h**2
LambdaCDM = Class()
LambdaCDM.set({
'omega_b': ombh2,
'omega_cdm': omch2,
'h': h,
'A_s': A_s,
'n_s': n_s
})
LambdaCDM.set({
'output': 'mPk,sCl',
'P_k_max_1/Mpc': 10.0,
'l_switch_limber': 100,
'selection': 'dirac',
'selection_mean': zs,
'l_max_lss': lmax,
'non linear': 'halofit'
})
# run class
LambdaCDM.compute()
si8 = LambdaCDM.sigma8()
cls = LambdaCDM.density_cl(lmax)
ell = cls['ell'][2:]
clphiphi = cls['ll'][0][2:]
clkk = 1.0 / 4 * (ell + 2.0) * (ell + 1.0) * (ell) * (ell - 1.0) * clphiphi
return si8, ell, clkk
def __init__(self, NSIDE, As):
self.NSIDE = NSIDE
self.Npix = 12 * NSIDE**2
self.As = As
print("Initialising sampler")
self.cosmo = Class()
#print("Maps")
#A recommenter
#self.Qs, self.Us, self.sigma_Qs, self.sigma_Us = aggregate_by_pixels_params(get_pixels_params(self.NSIDE))
#print("betas")
self.matrix_mean, self.matrix_var = aggregate_mixing_params(
get_mixing_matrix_params(self.NSIDE))
print("Cosmo params")
self.cosmo_means = np.array(COSMO_PARAMS_MEANS)
self.cosmo_stdd = np.diag(COSMO_PARAMS_SIGMA)
self.instrument = pysm.Instrument(
get_instrument('litebird', self.NSIDE))
self.components = [CMB(), Dust(150.), Synchrotron(150.)]
self.mixing_matrix = MixingMatrix(*self.components)
self.mixing_matrix_evaluator = self.mixing_matrix.evaluator(
self.instrument.Frequencies)
self.noise_covar_one_pix = self.noise_covariance_in_freq(self.NSIDE)
#A recommenter
#self.noise_stdd_all = np.concatenate([np.sqrt(self.noise_covar_one_pix) for _ in range(2*self.Npix)])
print("End of initialisation")
def __setstate__(self, state):
self.__dict__.update(state)
self.cosmo = Class()
self.components = [CMB(), Dust(150.), Synchrotron(150.)]
self.mixing_matrix = MixingMatrix(*self.components)
self.mixing_matrix_evaluator = self.mixing_matrix.evaluator(
self.instrument.Frequencies)
def __init__(self, NSIDE):
self.NSIDE = NSIDE
self.Npix = 12 * NSIDE**2
print("Initialising sampler")
self.cosmo = Class()
print("Maps")
self.templates_map, self.templates_var = aggregate_pixels_params(
get_pixels_params(self.NSIDE))
print("betas")
self.matrix_mean, self.matrix_var = aggregate_mixing_params(
get_mixing_matrix_params(self.NSIDE))
print("Cosmo params")
self.cosmo_means = np.array(COSMO_PARAMS_MEANS)
self.cosmo_var = (np.diag(COSMO_PARAMS_SIGMA) / 2)**2
plt.hist(self.templates_map)
plt.savefig("mean_values.png")
plt.close()
plt.hist(self.templates_var)
plt.savefig("std_values.png")
plt.close()
self.instrument = pysm.Instrument(
get_instrument('litebird', self.NSIDE))
self.components = [CMB(), Dust(150.), Synchrotron(150.)]
self.mixing_matrix = MixingMatrix(*self.components)
self.mixing_matrix_evaluator = self.mixing_matrix.evaluator(
self.instrument.Frequencies)
print("End of initialisation")
def test_class_setup():
cosmology = astropy.cosmology.Planck13
assert cosmology.Om0 == cosmology.Odm0 + cosmology.Ob0
assert 1 == (cosmology.Om0 + cosmology.Ode0 + cosmology.Ok0 +
cosmology.Ogamma0 + cosmology.Onu0)
class_parameters = get_class_parameters(cosmology)
try:
from classy import Class
cosmo = Class()
cosmo.set(class_parameters)
cosmo.compute()
assert cosmo.h() == cosmology.h
assert cosmo.T_cmb() == cosmology.Tcmb0.value
assert cosmo.Omega_b() == cosmology.Ob0
# Calculate Omega(CDM)_0 two ways:
assert abs((cosmo.Omega_m() - cosmo.Omega_b()) -
(cosmology.Odm0 - cosmology.Onu0)) < 1e-8
assert abs(cosmo.Omega_m() - (cosmology.Om0 - cosmology.Onu0)) < 1e-8
# CLASS calculates Omega_Lambda itself so this is a non-trivial test.
calculated_Ode0 = cosmo.get_current_derived_parameters(
['Omega_Lambda'])['Omega_Lambda']
assert abs(calculated_Ode0 - (cosmology.Ode0 + cosmology.Onu0)) < 1e-5
cosmo.struct_cleanup()
cosmo.empty()
except ImportError:
pass
def cosmo(self):
""" """
if not self._cosmo:
self._cosmo = Class()
self._cosmo.set(self.class_params)
self.logger.info("Initializing Class")
self._cosmo.compute()
if self.params['fix_sigma8']:
sig8 = self._cosmo.sigma8()
A_s = self._cosmo.pars['A_s']
self._cosmo.struct_cleanup()
# renormalize to fix sig8
self.A_s = A_s * (self.params['sigma8'] * 1. / sig8)**2
self._cosmo.set(A_s=self.A_s)
self._cosmo.compute()
sig8 = self._cosmo.sigma8()
self.params['sigma8'] = sig8
self.params['A_s'] = self._cosmo.pars['A_s']
self.params[
'sigma8z'] = sig8 * self._cosmo.scale_independent_growth_factor(
self.class_params['z_pk'])
self.params['f'] = self._cosmo.scale_independent_growth_factor_f(
self.class_params['z_pk'])
self.logger.info(f" z: {self.class_params['z_pk']}")
self.logger.info(f" sigma_8: {self.params['sigma8']}")
self.logger.info(f" sigma_8(z): {self.params['sigma8z']}")
self.logger.info(f" f(z): {self.params['f']}")
self.logger.info(
f"f sigma8(z): {self.params['f']*self.params['sigma8z']}")
return self._cosmo
def grid_cosmo(self,
parameter,
parameter_grid,
classy_dict,
verbose=False):
"""
Compute a grid of cosmologies, varying one parameter over a grid.
Parameters
----------
parameter (string) : name of parameter in the CLASS dict
parameter_grid (list of float) : grid over which the parameter will
be varied
classy_dict (dictionary) : base dictionary to be copied for each
cosmology evaluation
Returns
-------
list of CLASS objects : contains a list of computed cosmologies,
as a result of varying the parameter over the grid
"""
cosmo_list = []
for grid_value in parameter_grid:
cosmo = Class()
temp_dict = classy_dict.copy()
temp_dict[parameter] = grid_value
if verbose:
print(temp_dict)
cosmo.set(temp_dict)
cosmo.compute()
cosmo_list.append(cosmo)
return cosmo_list
def run_class(parameters, gettransfer):
'''
Run CLASS with the input parameters and return the perturbations and
the value of tau_0 (which should be fixed but we still return the value
for the purpose of checking) and the earliest transfer (if asked). Print the
amount of time taken to run.
Args:
parameters: parameters to run CLASS
gettransfer (boolean): whether to get the earliest transfer
Return: (pts, tau_0) if gettransfer=False and (pts, tau_0, transfer) otherwise
'''
start_time = time.time()
cosmo = Class()
cosmo.set(parameters)
cosmo.compute()
pts = cosmo.get_perturbations()['scalar']
tau_0 = cosmo.get_current_derived_parameters(['conformal_age'])['conformal_age']
print("--- %s seconds ---" % (time.time() - start_time))
# 45999 is the largest redshift possible
if (gettransfer):
tf = cosmo.get_transfer(45999)
return pts, tau_0, tf
return pts, tau_0
def get_cls(params=None,lmax=3000,accurate=False,engine='camb',de='ppf',nonlinear=True,save_raw_camb_output=None):
from camb import model
params = map_params(params,engine=engine)
if engine=='camb':
pars = set_camb_pars(params=params,de=de)
if accurate:
pars.set_accuracy(AccuracyBoost=2.0, lSampleBoost=2.0, lAccuracyBoost=2.0, DoLateRadTruncation=False)
pars.set_for_lmax(lmax=int(lmax+500), lens_potential_accuracy=8.0, lens_margin=2050, max_eta_k=20000)
else:
pars.set_accuracy(AccuracyBoost=1.0, lSampleBoost=1.0, lAccuracyBoost=1.0)
pars.set_for_lmax(lmax=int(lmax+500), lens_potential_accuracy=1, max_eta_k=2*lmax)
if nonlinear:
pars.NonLinear = model.NonLinear_both
pars.NonLinearModel.set_params('mead2016', HMCode_A_baryon = 3.13, HMCode_eta_baryon = 0.603)
else:
pars.NonLinear = model.NonLinear_none
return load_theory(pars,lpad=lmax+2,save_raw_camb_output=save_raw_camb_output)
elif engine=='class':
from classy import Class
cosmo = Class()
params['output'] = 'lCl,tCl,pCl'
params['lensing'] = 'yes'
params['non linear'] = 'hmcode'
params['l_max_scalars'] = lmax
cosmo.set(params)
cosmo.compute()
return retval
def setup(self):
"""
Create an instance of Class and attach it to self.
"""
self.cosmo = Class()
self.cosmo.set(self.constants)
self.cosmo.compute()
self.cosmo.struct_cleanup()
def initialize(self):
"""Importing CLASS from the correct path, if given, and if not, globally."""
# If path not given, try using general path to modules
if not self.path and self.path_install:
self.path = self.get_path(self.path_install)
if self.path:
self.log.info("Importing *local* classy from " + self.path)
classy_build_path = os.path.join(self.path, "python", "build")
post = next(d for d in os.listdir(classy_build_path)
if d.startswith("lib."))
classy_build_path = os.path.join(classy_build_path, post)
if not os.path.exists(classy_build_path):
# If path was given as an install path, try to install global one anyway
if self.path_install:
self.log.info(
"Importing *global* CLASS (because not installed).")
else:
raise LoggedError(
self.log, "Either CLASS is not in the given folder, "
"'%s', or you have not compiled it.", self.path)
else:
# Inserting the previously found path into the list of import folders
sys.path.insert(0, classy_build_path)
else:
self.log.info("Importing *global* CLASS.")
try:
from classy import Class, CosmoSevereError, CosmoComputationError
except ImportError:
raise LoggedError(
self.log, "Couldn't find the CLASS python interface. "
"Make sure that you have compiled it, and that you either\n"
" (a) specify a path (you didn't) or\n"
" (b) install the Python interface globally with\n"
" '/path/to/class/python/python setup.py install --user'")
self.classy = Class()
# Propagate errors up
global CosmoComputationError, CosmoSevereError
# Generate states, to avoid recomputing
self.n_states = 3
self.states = [{
"params": None,
"derived": None,
"derived_extra": None,
"last": 0
} for i in range(self.n_states)]
# Dict of named tuples to collect requirements and computation methods
self.collectors = {}
# Additional input parameters to pass to CLASS
self.extra_args = deepcopy_where_possible(self.extra_args) or {}
# Add general CLASS stuff
self.extra_args["output"] = self.extra_args.get("output", "")
if "sBBN file" in self.extra_args:
self.extra_args["sBBN file"] = (
self.extra_args["sBBN file"].format(classy=self.path))
# Set aliases
self.planck_to_classy = self.renames
# Derived parameters that may not have been requested, but will be necessary later
self.derived_extra = []
def __init__(self):
""" """
self.data = {}
omm = class_params['Omega_b'] + class_params['Omega_cdm']
logging.info("Omega_m = %f" % omm)
self.dist = FlatLambdaCDM(100, omm)
self.cosmo = Class()
self.cosmo.set(class_params)
self.cosmo.compute()
def fetch_planck_cosmology(z_max):
cosmo = Class()
cosmo.set({'H0' : 67.36, 'T_cmb' : 2.7255, 'A_s' : 2.14e-9, 'n_s' : 0.9649,
'reio_parametrization' : 'reio_camb', 'tau_reio' : 0.0544, 'reionization_exponent' : 1.5,
'reionization_width' : 0.5, 'helium_fullreio_redshift' : 3.5, 'helium_fullreio_width' : 0.5,
'omega_b' : 0.02237, 'omega_cdm' : 0.1200, 'Omega_k' : 0., 'Omega_Lambda' : 0.6847,
'output' : 'mPk', 'P_k_max_1/Mpc' : 100, 'z_max_pk' : z_max, 'non linear' : 'halofit'})
cosmo.compute()
return cosmo
def initialize(self):
"""Importing CLASS from the correct path, if given, and if not, globally."""
# If path not given, try using general path to external packages
if not self.path and self.packages_path:
self.path = self.get_path(self.packages_path)
if self.path:
self.log.info("Importing *local* classy from " + self.path)
classy_build_path = os.path.join(self.path, "python", "build")
py_version = "%d.%d" % (sys.version_info.major,
sys.version_info.minor)
try:
post = next(d for d in os.listdir(classy_build_path)
if (d.startswith("lib.") and py_version in d))
classy_build_path = os.path.join(classy_build_path, post)
if not os.path.exists(classy_build_path):
# If path was given as an install path, try to load global one anyway
if self.packages_path:
self.log.info(
"Importing *global* CLASS (because not compiled?)."
)
else:
raise StopIteration
except StopIteration:
raise LoggedError(
self.log, "Either CLASS is not in the given folder, "
"'%s', or you have not compiled it.", self.path)
else:
classy_build_path = None
self.log.info("Importing *global* CLASS.")
try:
self.classy_module = load_module(
'classy',
path=classy_build_path,
min_version=self._classy_repo_version)
from classy import Class, CosmoSevereError, CosmoComputationError
except ImportError:
raise LoggedError(
self.log, "Couldn't find the CLASS python interface. "
"Make sure that you have compiled it, and that you either\n"
" (a) specify a path (you didn't) or\n"
" (b) install the Python interface globally with\n"
" '/path/to/class/python/python setup.py install --user'")
except VersionCheckError as e:
raise LoggedError(self.log, str(e))
self.classy = Class()
# Propagate errors up
global CosmoComputationError, CosmoSevereError
super().initialize()
# Add general CLASS stuff
self.extra_args["output"] = self.extra_args.get("output", "")
if "sBBN file" in self.extra_args:
self.extra_args["sBBN file"] = (
self.extra_args["sBBN file"].format(classy=self.path))
# Derived parameters that may not have been requested, but will be necessary later
self.derived_extra = []
def get_cosmo(i):
obh2, och2, w, ns, ln10As, H0, Neff, s8 = AD.test_box_cosmologies()[i]
h = H0/100.
Omega_b = obh2/h**2
Omega_c = och2/h**2
Omega_m = Omega_b+Omega_c
params = {'output': 'mPk', 'h': h, 'ln10^{10}A_s': ln10As, 'n_s': ns, 'w0_fld': w, 'wa_fld': 0.0, 'Omega_b': Omega_b, 'Omega_cdm': Omega_c, 'Omega_Lambda': 1.- Omega_m, 'N_eff': Neff, 'P_k_max_1/Mpc':10., 'z_max_pk':10. }
cosmo = Class()
cosmo.set(params)
cosmo.compute()
return cosmo, h, Omega_m
def setUp(self):
"""
set up data used in the tests.
setUp is called before each test function execution.
"""
self.cosmo = Class()
self.cosmo_newt = Class()
self.verbose = {
'input_verbose': 1,
'background_verbose': 1,
'thermodynamics_verbose': 1,
'perturbations_verbose': 1,
'transfer_verbose': 1,
'primordial_verbose': 1,
'spectra_verbose': 1,
'nonlinear_verbose': 1,
'lensing_verbose': 1,
'output_verbose': 1
}
self.scenario = {}
def do_model_setup(self, params):
""" Method to calculate the power spectrum primordial and
lensing templates for a given set of cosmological parameters.
This computation requires that the lmax be set much higher
that the lmax required in the final analys.s
Parameters
----------
params: dict
Dictionary of cosmological parameters to be sent to CLASS.
Returns
-------
tuple(array_like(float))
Tuple containing the BB primordial and lensing templates.
"""
try:
params.pop('a_lens')
except:
pass
params.update({
'output': 'tCl pCl lCl',
'l_max_scalars': 5000,
'l_max_tensors': 2000,
'modes': 's, t',
'r': 1,
'lensing': 'yes',
})
cosm = Class()
cosm.set(params)
cosm.compute()
# get the lensed and raw power spectra up to the maximum
# multipole used in the likelihood analysis. Multiply by
# T_CMB ^ 2 to get from dimensionless to uK^2 units.
lensed_cls = cosm.lensed_cl(3 * self.nside - 1)['bb'] * (2.7225e6)**2
raw_cls = cosm.raw_cl(3 * self.nside - 1)['bb'] * (2.7225e6)**2
# get ells, used in the calculation of the foreground model
# over the same range.
ells = cosm.raw_cl(3 * self.nside - 1)['ell']
# do the house keeping for the CLASS code.
cosm.struct_cleanup()
cosm.empty()
# calculate the lensing-only template
lens_template = self.apply_coupling(lensed_cls - raw_cls)
raw_cls = self.apply_coupling(raw_cls)
# now separately do the foreground template setup.
if self.marg:
fg_template = np.zeros(3 * self.nside)
fg_template[1:] = (ells[1:] / 80.)**-2.4
fg_template = self.apply_coupling(fg_template)
return (raw_cls, lens_template, fg_template)
return (raw_cls, lens_template)
def set_cosmology(self, params):
"""
Set the cosmological parameters of the emulator. One must
call this function before actually computing mass functions.
:param params:
A dictionary of parameters, where the key is the parameter name and
value is its value.
:return:
None
"""
try:
from classy import Class
except ImportError:
print("Class not installed. Cannot compute the mass function " +
"directly, only predict " +
"parameters from the GPs using the predict() function.")
return
self.mf_slopes_and_intercepts = self.predict(params)
#Set up a CLASS dictionary
self.h = params['H0'] / 100.
self.Omega_m = (params["omega_b"] + params["omega_cdm"]) / self.h**2
class_cosmology = {
'output': 'mPk',
'H0': params['H0'],
'ln10^{10}A_s': params['ln10As'],
'n_s': params['n_s'],
'w0_fld': params['w0'],
'wa_fld': 0.0,
'omega_b': params['omega_b'],
'omega_cdm': params['omega_cdm'],
'Omega_Lambda': 1 - self.Omega_m,
'N_eff': params['N_eff'],
'P_k_max_1/Mpc': 10.,
'z_max_pk': 5.03
}
#Seed splines in CLASS
cc = Class()
cc.set(class_cosmology)
cc.compute()
#Make everything attributes
self.cc = cc
self.k = np.logspace(-5, 1, num=1000) # Mpc^-1 comoving
self.M = np.logspace(10, 16.5, num=1000) # Msun/h
self.computed_sigma2 = {}
self.computed_dsigma2dM = {}
self.computed_sigma2_splines = {}
self.computed_dsigma2dM_splines = {}
self.computed_pk = {}
self.cosmology_is_set = True
return
def __init__(self):
# print 'Class for tSZ Cl'
# self.ptilde = np.loadtxt(LIBDIR+'/aux_files/ptilde.txt')
self.fort_lib_cl = cdll.LoadLibrary(LIBDIR + "/source/calc_cl")
self.fort_lib_cl.calc_cl_.argtypes = [
POINTER(c_double), #h0
POINTER(c_double), #obh2
POINTER(c_double), #och2
POINTER(c_double), #mnu
POINTER(c_double), #bias
POINTER(c_double), #Mcut
POINTER(c_double), #M1
POINTER(c_double), #kappa
POINTER(c_double), #sigma_Ncen
POINTER(c_double), #alp_Nsat
POINTER(c_double), #rmax
POINTER(c_double), #rgs
POINTER(c_int64), #pk_nk
POINTER(c_int64), #pk_nz
np.ctypeslib.ndpointer(dtype=np.double), #karr
np.ctypeslib.ndpointer(dtype=np.double), #pkarr
np.ctypeslib.ndpointer(dtype=np.double), #dndz
POINTER(c_int64), #nz_dndz
POINTER(c_double), #z1
POINTER(c_double), #z2
POINTER(c_double), #z1_ng
POINTER(c_double), #z2_ng
POINTER(c_int64), #nl
np.ctypeslib.ndpointer(dtype=np.double), #ell
np.ctypeslib.ndpointer(dtype=np.double), #gg
np.ctypeslib.ndpointer(dtype=np.double), #gy
np.ctypeslib.ndpointer(dtype=np.double), #tll
POINTER(c_double), #ng(z1<z<z2)
POINTER(c_int64), #flag_nu
POINTER(c_int64), #flag_tll
POINTER(c_int64), #nm
POINTER(c_int64) #nz
]
self.fort_lib_cl.calc_cl_.restype = c_void_p
# Calcualtion setup
self.kmin = 1e-3
self.kmax = 5.
self.zmax = 4. # should be consistent with fortran code
self.nk_pk = 200
self.nz_pk = 51
# Class
self.cosmo = Class()
def initialize_class_inst(self):
""" Get electron ionization fraction from CLASS
"""
class_parameters = {
"H0": self.cosmo.H0.value,
"Omega_b": self.cosmo.Ob0,
"N_ur": self.cosmo.Neff,
"Omega_cdm": self.cosmo.Odm0,
"YHe": self.Y_p,
"z_reio": self.z_reio
}
self.CLASS_inst = Class()
self.CLASS_inst.set(class_parameters)
self.CLASS_inst.compute()
def get_cosmo(self, classy_dict):
"""
Compute a cosmology with CLASS.
This is purely for convenience.
Parameters
----------
classy_dict (dictionary) : contains the inputs for the CLASS python
wrapper such as 'output' and 'l_max_scalars'.
"""
cosmo = Class()
cosmo.set(classy_dict)
cosmo.compute()
return cosmo
def get_cosmo(i):
obh2, och2, w, ns, ln10As, H0, Neff, s8 = AD.building_box_cosmologies()[i]
aemcosmo={'Obh2':obh2, 'Och2':och2, 'w0':w, 'n_s':ns, 'ln10^{10}A_s':ln10As, 'N_eff':Neff, 'H0':H0}
import aemHMF
hmf = aemHMF.Aemulus_HMF()
hmf.set_cosmology(aemcosmo)
h = H0/100.
Omega_b = obh2/h**2
Omega_c = och2/h**2
Omega_m = Omega_b+Omega_c
params = {'output': 'mPk', 'h': h, 'ln10^{10}A_s': ln10As, 'n_s': ns, 'w0_fld': w, 'wa_fld': 0.0, 'Omega_b': Omega_b, 'Omega_cdm': Omega_c, 'Omega_Lambda': 1.- Omega_m, 'N_eff': Neff, 'P_k_max_1/Mpc':10., 'z_max_pk':10. }
cosmo = Class()
cosmo.set(params)
cosmo.compute()
return cosmo, h, Omega_m, hmf
def class_spectrum(params):
""" Function to generate the theoretical CMB power spectrum for a
given set of parameters.
Parameters
----------
params: dict
dict containing the parameters expected by CLASS and their values.
Returns
-------
array_like(float)
Array of shape (4, lmax + 1) containing the TT, EE, BB, TE power
spectra.
"""
# This line is crucial to prevent pop from removing the lensing efficieny
# from future runs in the same script.
class_pars = {**params}
try:
# if lensing amplitude is set, remove it from dictionary that will
# be passed to CLASS.
a_lens = class_pars.pop('a_lens')
except KeyError:
# if not set in params dictionary just set to 1.
a_lens = 1
# generate the CMB realizations.
print("Running CLASS with lensing efficiency: ", a_lens)
cos = Class()
cos.set(class_pars)
cos.compute()
# returns CLASS format, 0 to lmax, dimensionless, Cls.
# multiply by (2.7225e6) ** 2 to get in uK_CMB ^ 2
lensed_cls = cos.lensed_cl()
raw_bb = cos.raw_cl()['bb'][:class_pars['l_max_scalars'] + 1]
# calculate the lensing contribution to BB and rescale by
# the lensing amplitude factor.
lensing_bb = a_lens * (lensed_cls['bb'] - raw_bb)
cos.struct_cleanup()
cos.empty()
synfast_cls = np.zeros((4, class_pars['l_max_scalars'] + 1))
synfast_cls[0, :] = lensed_cls['tt']
synfast_cls[1, :] = lensed_cls['ee']
synfast_cls[2, :] = lensing_bb + raw_bb
synfast_cls[3, :] = lensed_cls['te']
return synfast_cls * (2.7225e6)**2
