def GetSpectra(params, type='lensed', lmax=2000):
cosmo = classy.Class()
cosmo.set(params)
cosmo.compute()
computed_cls = cosmo.lensed_cl(lmax)
c2d = computed_cls['ell'] * (computed_cls['ell'] +
1) / 2. / np.pi * (2.725e6**2)
cosmo.struct_cleanup()
cosmo.empty()
return computed_cls, c2d
def setup(options):
# Read options from the ini file which are fixed across
# the length of the chain
config = {
'lmax': options.get_int(option_section, 'lmax', default=2000),
'zmax': options.get_double(option_section, 'zmax', default=4.0),
'kmax': options.get_double(option_section, 'kmax', default=50.0),
'debug': options.get_bool(option_section, 'debug', default=False),
}
# Create the object that connects to Class
config['cosmo'] = classy.Class()
# Return all this config information
return config
def initialise(paths):
"""
Initialisation routine
This function take the paths,
initialise a :class:`data` instance, a cosmological code instance.
Parameters
----------
paths: dict
paths to all the necessary files
"""
# Report the cosmological code version.
# Official version number
common_file_path = os.path.join(
paths['cosmo'], 'include', 'common.h')
with open(common_file_path, 'r') as common_file:
for line in common_file:
if line.find('_VERSION_') != -1:
version = line.split()[-1].replace('"', '')
break
print('with CLASS %s' % version)
# initialise data class
data = Data(paths)
# Loading up the cosmological backbone.
# For the moment, only CLASS has been wrapped.
classy_path = ''
for elem in os.listdir(os.path.join(
paths['cosmo'], "python", "build")):
if elem.find("lib.") != -1:
classy_path = os.path.join(
paths['cosmo'], "python", "build", elem)
break
# Inserting the previously found path into the list of folders to
# search for python modules.
sys.path.insert(1, classy_path)
import classy
cosmo = classy.Class()
print('classy location', classy.__file__)
return cosmo, data
def setup(options):
# Read options from the ini file which are fixed across
# the length of the chain
config = {
'lmax': options.get_int(option_section, 'lmax', default=2000),
'zmax': options.get_double(option_section, 'zmax', default=4.0),
'kmax': options.get_double(option_section, 'kmax', default=50.0),
'debug': options.get_bool(option_section, 'debug', default=False),
'lensing': options.get_bool(option_section, 'lensing', default=True),
}
for _, key in options.keys(option_section):
if key.startswith('class_'):
config[key] = block[option_section, key]
# Create the object that connects to Class
config['cosmo'] = classy.Class()
# Return all this config information
return config
def setup(options):
#Read options from the ini file which are fixed across
#the length of the chain
config = {
'lmax':
options.get_int(option_section, 'lmax', default=2500),
'zmax':
options.get_double(option_section, 'zmax', default=3.),
#'zmax_pk': options.get_double(option_section,'zmax_pk', default=3.), #TB experiment
'kmax':
options.get_double(option_section, 'kmax', default=1.0),
'debug':
options.get_bool(option_section, 'debug', default=False),
'lensing':
options.get_string(option_section, 'lensing', default='yes'),
'expansion_model':
options.get_string(option_section, 'expansion_model', default='lcdm'),
'gravity_model':
options.get_string(option_section,
'gravity_model',
default='propto_omega'),
'modes':
options.get_string(option_section, 'modes', default='s'),
'output':
options.get_string(option_section,
'output',
default='tCl,lCl,pCl,mPk,mTk'),
'sBBN file':
options.get_string(option_section, 'sBBN_file'),
'k_output_values':
options.get_string(option_section, 'k_output_values', default='')
#'skip_stability_tests_smg': options.get_string(option_section, 'skip_stability_tests_smg', default = 'no'),
#'background_verbose': options.get_int(option_section,'background_verbose', default=1),
#'thermodynamics_verbose': options.get_int(option_section,'thermodynamics_verbose', default=10)
#'kineticity_safe_smg': options.get_double(option_section,'kineticity_safe_smg', default=1e-5)
}
#Create the object that connects to Class
config['cosmo'] = classy.Class()
#Return all this config information
return config
"h": p['h'],
"Omega_cdm": p['omegaM'] - p['omegaB'],
"Omega_b": p['omegaB'],
"A_s": 2e-9,
"n_s": 1.0,
# "w0_fld" : -1.0,
# "wa_fld" : 0.0,
"back_integration_stepsize":
1.0e-3, # Req. for agreement at <1e-5 with CCL.
"Omega_k": 0.0,
"N_ur": 2.0, # normally 3
"N_ncdm": 1, # 1 species
"m_ncdm": 0.06
}
cosm = classy.Class()
cosm.set(class_params)
cosm.compute()
print cosm.angular_distance(1090.)
print cosm.Omega_g() * cosm.h()**2. #, cosmo.Omega0_m()
#help(cosm)
exit()
z_class = cosm.get_background()["z"][:]
a_class = 1. / (1. + z_class)
DA_class = cosm.get_background()["ang.diam.dist."][:]
#d_co = cosm.get_background()["comov. dist."][:]
cosm.struct_cleanup()
# Calculate using our code
def get_class_pk_lin(cosmo):
"""Run CLASS and return the linear power spectrum.
Args:
cosmo (:class:`~pyccl.core.Cosmology`): Cosmological
parameters. The cosmological parameters with
which to run CLASS.
Returns:
:class:`~pyccl.pk2d.Pk2D`: Power spectrum object.\
The linear power spectrum.
"""
assert HAVE_CLASS, ("You must have the python wrapper for CLASS "
"installed to run CCL with CLASS!")
params = {
"output": "mPk",
"non linear": "none",
"P_k_max_1/Mpc": cosmo.cosmo.spline_params.K_MAX_SPLINE,
"z_max_pk": 1.0 / cosmo.cosmo.spline_params.A_SPLINE_MINLOG_PK - 1.0,
"modes": "s",
"lensing": "no",
"h": cosmo["h"],
"Omega_cdm": cosmo["Omega_c"],
"Omega_b": cosmo["Omega_b"],
"Omega_k": cosmo["Omega_k"],
"n_s": cosmo["n_s"]
}
# cosmological constant?
# set Omega_Lambda = 0.0 if w !=-1 or wa != 0
if cosmo['w0'] != -1 or cosmo['wa'] != 0:
params["Omega_Lambda"] = 0
params['w0_fld'] = cosmo['w0']
params['wa_fld'] = cosmo['wa']
# neutrino parameters
# massless neutrinos
if cosmo["N_nu_rel"] > 1e-4:
params["N_ur"] = cosmo["N_nu_rel"]
else:
params["N_ur"] = 0.0
# massive neutrinos
if cosmo["N_nu_mass"] > 0:
params["N_ncdm"] = cosmo["N_nu_mass"]
masses = lib.parameters_get_nu_masses(cosmo._params, 3)
params["m_ncdm"] = ", ".join(
["%g" % m for m in masses[:cosmo["N_nu_mass"]]])
params["T_cmb"] = cosmo["T_CMB"]
# if we have sigma8, we need to find A_s
if np.isfinite(cosmo["A_s"]):
params["A_s"] = cosmo["A_s"]
elif np.isfinite(cosmo["sigma8"]):
# in this case, CCL will internally normalize for us when we init
# the linear power spectrum - so we just get close
A_s_fid = 2.43e-9 * (cosmo["sigma8"] / 0.87659)**2
params["A_s"] = A_s_fid
else:
raise CCLError("Could not normalize the linear power spectrum! "
"A_s = %f, sigma8 = %f" %
(cosmo['A_s'], cosmo['sigma8']))
model = None
try:
model = classy.Class()
model.set(params)
model.compute()
# Set k and a sampling from CCL parameters
nk = lib.get_pk_spline_nk(cosmo.cosmo)
na = lib.get_pk_spline_na(cosmo.cosmo)
status = 0
a_arr, status = lib.get_pk_spline_a(cosmo.cosmo, na, status)
check(status)
# FIXME - getting the lowest CLASS k value from the python interface
# appears to be broken - setting to 1e-5 which is close to the
# old value
lk_arr = np.log(
np.logspace(-5, np.log10(cosmo.cosmo.spline_params.K_MAX_SPLINE),
nk))
# we need to cut this to the max value used for calling CLASS
msk = lk_arr < np.log(cosmo.cosmo.spline_params.K_MAX_SPLINE)
nk = int(np.sum(msk))
lk_arr = lk_arr[msk]
# now do interp by hand
ln_p_k_and_z = np.zeros((na, nk), dtype=np.float64)
for aind in range(na):
z = max(1.0 / a_arr[aind] - 1, 1e-10)
for kind in range(nk):
ln_p_k_and_z[aind, kind] = np.log(
model.pk_lin(np.exp(lk_arr[kind]), z))
finally:
if model is not None:
model.struct_cleanup()
model.empty()
params["P_k_max_1/Mpc"] = cosmo.cosmo.spline_params.K_MAX_SPLINE
# make the Pk2D object
pk_lin = Pk2D(pkfunc=None,
a_arr=a_arr,
lk_arr=lk_arr,
pk_arr=ln_p_k_and_z,
is_logp=True,
extrap_order_lok=1,
extrap_order_hik=2,
cosmo=cosmo)
return pk_lin
'T_cmb': self.Tcmb, 'tau_reio': self.tau_reio, 'YHe': self.YHe,
'reionization_exponent': self.reion_exponent,
'reionization_width': self.reion_width, 'P_k_max_1/Mpc': 200,
'output': 'dTk,mPk,tCl',
}
# m_dmeff = 1.0 # GeV
# sigma_dmeff = 1e-41 # cm^2
# Vrel_dmeff = 30 # km/s at z ~ 1010
params = LCDM.class_params()
z_pk = 60.
params.update({'z_pk': z_pk})
cl = classy.Class()
cl.set(params)
cl.compute()
import pyccl as ccl
cosmo = ccl.Cosmology(Omega_c=params['omega_cdm'],
Omega_b=params['omega_b'],
h=params['h'],
n_s=params['n_s'], A_s=params['A_s'])
# trying to adapt from here: https://github.com/damonge/ShCl_like/blob/0350b0cca5de51cd92efb8e27be292bbbb449bce/shcl_like/clccl.py#L141
z_bg = np.concatenate((np.linspace(0, 10, 100), np.geomspace(10, 1500, 50)))
a = 1/(1 + z_bg[::-1])
distance = cl.z_of_r(z_bg)
def classy(self):
if self._classy is None:
self._classy = classy.Class()
self._classy.set(self.cosmo.class_params)
self._classy.compute()
return self._classy
