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 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 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 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 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 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 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 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 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 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 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 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 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 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
def calculate_spectra(self, cosmo_params, force_recalc=False):
settings = cosmo_params.copy()
settings.update({
"output": "tCl,mPk",
"evolver": "1",
"gauge": "newtonian",
"P_k_max_1/Mpc": 10,
})
database = Database(config.DATABASE_DIR, "spectra.dat")
if settings in database and not force_recalc:
data = database[settings]
ell = data["ell"]
tt = data["tt"]
kh = data["kh"]
Pkh = data["Pkh"]
self.z_rec = data["z_rec"]
else:
cosmo = Class()
cosmo.set(settings)
cosmo.compute()
# Cl's
data = cosmo.raw_cl()
ell = data["ell"]
tt = data["tt"]
# Matter spectrum
k = np.logspace(-3, 1, config.MATTER_SPECTRUM_CLIENT_SAMPLES_PER_DECADE * 4)
Pk = np.vectorize(cosmo.pk)(k, 0)
kh = k * cosmo.h()
Pkh = Pk / cosmo.h()**3
# Get redshift of decoupling
z_rec = cosmo.get_current_derived_parameters(['z_rec'])['z_rec']
self.z_rec = z_rec
# Store to database
database[settings] = {
"ell": data["ell"],
"tt": data["tt"],
"kh": k,
"Pkh": Pk,
"z_rec": z_rec,
}
return ClSpectrum(ell[2:], tt[2:]), PkSpectrum(kh, Pkh)
class ClassCoreModule(object):
def __init__(self, mapping=DEFAULT_PARAM_MAPPING, constants=CLASS_DEFAULT_PARAMS):
"""
Core Module for the delegation of the computation of the cmb power
spectrum to the Class wrapper classy.
The defaults are for the 6 LambdaCDM cosmological parameters.
:param mapping: (optional) dict mapping name of the parameter to the index
:param constants: (optional) dict with constants overwriting CLASS defaults
"""
self.mapping = mapping
if constants is None:
constants = {}
self.constants = constants
def __call__(self, ctx):
p1 = ctx.getParams()
params = self.constants.copy()
for k,v in self.mapping.items():
params[k] = p1[v]
self.cosmo.set(params)
self.cosmo.compute()
if self.constants['lensing'] == 'yes':
cls = self.cosmo.lensed_cl()
else:
cls = self.cosmo.raw_cl()
Tcmb = self.cosmo.T_cmb()*1e6
frac = Tcmb**2 * cls['ell'][2:] * (cls['ell'][2:] + 1) / 2. / pi
ctx.add(CL_TT_KEY, frac*cls['tt'][2:])
ctx.add(CL_TE_KEY, frac*cls['te'][2:])
ctx.add(CL_EE_KEY, frac*cls['ee'][2:])
ctx.add(CL_BB_KEY, frac*cls['bb'][2:])
self.cosmo.struct_cleanup()
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()
class ClassCoreModule(object):
def __init__(self,
mapping=DEFAULT_PARAM_MAPPING,
constants=CLASS_DEFAULT_PARAMS):
"""
Core Module for the delegation of the computation of the cmb power
spectrum to the Class wrapper classy.
The defaults are for the 6 LambdaCDM cosmological parameters.
:param mapping: (optional) dict mapping name of the parameter to the index
:param constants: (optional) dict with constants overwriting CLASS defaults
"""
self.mapping = mapping
if constants is None:
constants = {}
self.constants = constants
def __call__(self, ctx):
p1 = ctx.getParams()
params = self.constants.copy()
for k, v in self.mapping.items():
params[k] = p1[v]
self.cosmo.set(params)
self.cosmo.compute()
if self.constants['lensing'] == 'yes':
cls = self.cosmo.lensed_cl()
else:
cls = self.cosmo.raw_cl()
Tcmb = self.cosmo.T_cmb() * 1e6
frac = Tcmb**2 * cls['ell'][2:] * (cls['ell'][2:] + 1) / 2. / pi
ctx.add(CL_TT_KEY, frac * cls['tt'][2:])
ctx.add(CL_TE_KEY, frac * cls['te'][2:])
ctx.add(CL_EE_KEY, frac * cls['ee'][2:])
ctx.add(CL_BB_KEY, frac * cls['bb'][2:])
self.cosmo.struct_cleanup()
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 calculate_power(cosmology,
k_min,
k_max,
z=0,
num_k=500,
scaled_by_h=True,
n_s=0.9619,
logA=3.0980):
"""
Calculate the power spectrum P(k,z) over the range k_min <= k <= k_max.
"""
try:
from classy import Class
cosmo = Class()
except ImportError:
raise RuntimeError('power.calculate_power requires classy.')
class_parameters = get_class_parameters(cosmology)
class_parameters['output'] = 'mPk'
if scaled_by_h:
class_parameters['P_k_max_h/Mpc'] = k_max
else:
class_parameters['P_k_max_1/Mpc'] = k_max
class_parameters['n_s'] = n_s
class_parameters['ln10^{10}A_s'] = logA
cosmo.set(class_parameters)
cosmo.compute()
if scaled_by_h:
k_scale = cosmo.h()
Pk_scale = cosmo.h()**3
else:
k_scale = 1.
Pk_scale = 1.
result = np.empty((num_k, ), dtype=[('k', float), ('Pk', float)])
result['k'][:] = np.logspace(np.log10(k_min), np.log10(k_max), num_k)
for i, k in enumerate(result['k']):
result['Pk'][i] = cosmo.pk(k * k_scale, z) * Pk_scale
cosmo.struct_cleanup()
cosmo.empty()
return result
def m_Pk(k=np.logspace(-3, 0., 100), z=0.53, nl_model='trg'):
print k
cosmo = Class()
CLASS_INPUT = {}
CLASS_INPUT['Mnu'] = ([{'N_eff': 0.0, 'N_ncdm': 1, 'm_ncdm': 0.06, 'deg_ncdm': 3.0}], 'normal')
CLASS_INPUT['Output_spectra'] = ([{'output': 'mPk', 'P_k_max_1/Mpc': 1, 'z_pk': z}], 'power')
CLASS_INPUT['Nonlinear'] = ([{'non linear': nl_model}], 'power')
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
# }
cosmo.struct_cleanup()
cosmo.empty()
INPUTPOWER = []
INPUTNORMAL = [{}]
for key, value in CLASS_INPUT.iteritems():
models, state = value
if state == 'power':
INPUTPOWER.append([{}]+models)
else:
INPUTNORMAL.extend(models)
PRODPOWER = list(itertools.product(*INPUTPOWER))
DICTARRAY = []
for normelem in INPUTNORMAL:
for powelem in PRODPOWER: # itertools.product(*modpower):
temp_dict = normelem.copy()
for elem in powelem:
temp_dict.update(elem)
DICTARRAY.append(temp_dict)
scenario = {}
for dic in DICTARRAY:
scenario.update(dic)
setting = cosmo.set(dict(verbose.items()+scenario.items()))
cosmo.compute()
pk_out = []
for k_i in k:
pk_out.append(cosmo.pk(k_i,z))
return pk_out
def get_theoretical_TT_TE_EE_unbinned_power_spec_D_ell(self, class_dict):
ellmin = self.lmin_class
ellmax = self.plmax
cosmo = Class()
cosmo.set(class_dict)
cosmo.compute()
cls = cosmo.lensed_cl(3000)
cosmo.struct_cleanup()
cosmo.empty()
#get in units of microkelvin squared
T_fac=(self.T_cmb*1e6)**2
ell=cls['ell']
D_fac=ell*(ell+1.)/(2*np.pi)
Dltt=(T_fac*D_fac*cls['tt'])[ellmin:ellmax+1]
Dlte=(T_fac*D_fac*cls['te'])[ellmin:ellmax+1]
Dlee=(T_fac*D_fac*cls['ee'])[ellmin:ellmax+1]
return cls['ell'][ellmin:ellmax+1], Dltt, Dlte, Dlee
def get_theory_pk(k, params):
"""Returns theoretical p(k) at z=0 given k values with given cosmo params
Parameters
----------
k : array-like
k values at which to evaluate theoretical p(k)
params : dict
Cosmological parameters to pass to Class
Returns
-------
Pk*norm : array
Theoretical p(k), with proper normalization
"""
# initialize class
cosmo = Class()
# set the class parameters
# cosmo.set(
# {
# "output": "mPk",
# "P_k_max_1/Mpc": 10.0,
# "omega_b": params["omega_b"],
# "omega_cdm": params["omega_cdm"],
# "h": params["h"],
# "A_s": params["A_s"],
# "n_s": params["n_s"],
# "tau_reio": params["tau_reio"],
# }
# )
cosmo.set({"output": "mPk", "P_k_max_1/Mpc": 10.0, **params})
# run class
cosmo.compute()
# calculate Pk at all k values (note the normalization for k and P_k)
h = params["h"]
return [cosmo.pk(kk * h, 0) * h**3 for kk in k]
def get_cls(params=None,
lmax=3000,
accurate=False,
engine='camb',
de='ppf',
nonlinear=True):
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=3.0,
lSampleBoost=1.0,
lAccuracyBoost=3.0)
pars.set_for_lmax(lmax=int(lmax + 500),
lens_potential_accuracy=3.0,
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
else:
pars.NonLinear = model.NonLinear_none
return load_theory(pars, lpad=lmax + 2)
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 loglkl(self, params):
cosmo = Class()
cosmo.set(params)
cosmo.compute()
chi2 = 0.
# for each point, compute angular distance da, radial distance dr,
# volume distance dv, sound horizon at baryon drag rs_d,
# theoretical prediction and chi2 contribution
for i in range(self.num_points):
da = cosmo.angular_distance(self.z[i])
dr = self.z[i] / cosmo.Hubble(self.z[i])
dv = pow(da * da * (1 + self.z[i]) * (1 + self.z[i]) * dr, 1. / 3.)
rs = cosmo.rs_drag()
if self.type[i] == 3:
theo = dv / rs
elif self.type[i] == 4:
theo = dv
elif self.type[i] == 5:
theo = da / rs
elif self.type[i] == 6:
theo = 1. / cosmo.Hubble(self.z[i]) / rs
elif self.type[i] == 7:
theo = rs / dv
chi2 += ((theo - self.data[i]) / self.error[i]) ** 2
# return ln(L)
# lkl = - 0.5 * chi2
# return -2ln(L)
lkl = chi2
return lkl
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 get_bao_rs_dV(zs,params=None,engine='camb',de='ppf'):
#FIXME: camb and class only agree at 3% level!!!
import camb
params = map_params(params,engine=engine)
if engine=='camb':
pars = set_camb_pars(params=params,de=de)
results = camb.get_results(pars)
retval = results.get_BAO(zs,pars)[:,0]
elif engine=='class':
from classy import Class
zs = np.asarray(zs)
cosmo = Class()
params['output'] = ''
cosmo.set(params)
cosmo.compute()
Hzs = np.array([cosmo.Hubble(z) for z in zs])
D_As = np.array([cosmo.angular_distance(z) for z in zs])
D_Vs = ((1+zs)**2 * D_As**2 * zs/Hzs)**(1/3.)
retval = cosmo.rs_drag()/D_Vs
cosmo.struct_cleanup()
cosmo.empty()
return retval
def ComputeTransferData(settings, redshift):
database_key = settings.copy()
database_key.update({'redshift': tuple(redshift)})
database = Database.Database(config.DATABASE_DIR)
if database_key in database:
return database[database_key], redshift
else:
cosmo = Class()
cosmo.set(settings)
cosmo.compute()
outputData = [cosmo.get_transfer(z) for z in redshift]
# Calculate d_g/4+psi
for transfer_function_dict in outputData:
transfer_function_dict["d_g/4 + psi"] = transfer_function_dict["d_g"]/4 + transfer_function_dict["psi"]
# Now filter the relevant fields
fields = TRANSFER_QUANTITIES + ["k (h/Mpc)"]
outputData = [{field: outputData[i][field] for field in fields} for i in range(len(redshift))]
database[database_key] = outputData
return outputData, redshift
def calculate_power(cosmology, k_min, k_max, z=0, num_k=500, scaled_by_h=True,
n_s=0.9619, logA=3.0980):
"""
Calculate the power spectrum P(k,z) over the range k_min <= k <= k_max.
"""
try:
from classy import Class
cosmo = Class()
except ImportError:
raise RuntimeError('power.calculate_power requires classy.')
class_parameters = get_class_parameters(cosmology)
class_parameters['output'] = 'mPk'
if scaled_by_h:
class_parameters['P_k_max_h/Mpc'] = k_max
else:
class_parameters['P_k_max_1/Mpc'] = k_max
class_parameters['n_s'] = n_s
class_parameters['ln10^{10}A_s'] = logA
cosmo.set(class_parameters)
cosmo.compute()
if scaled_by_h:
k_scale = cosmo.h()
Pk_scale = cosmo.h()**3
else:
k_scale = 1.
Pk_scale = 1.
result = np.empty((num_k,), dtype=[('k', float), ('Pk', float)])
result['k'][:] = np.logspace(np.log10(k_min), np.log10(k_max), num_k)
for i, k in enumerate(result['k']):
result['Pk'][i] = cosmo.pk(k * k_scale, z) * Pk_scale
cosmo.struct_cleanup()
cosmo.empty()
return result
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
**kwargs):
self.model.set(**kwargs)
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
def setup_class(self):
obh2, och2, w, ns, ln10As, H0, Neff = self.parameters
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,
#'sigma8':sigma8,
'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':5.,'non linear':'halofit'}
cosmo = Class()
cosmo.set(params)
print("CLASS is computing")
cosmo.compute()
print("\tCLASS done")
self.class_cosmo_object = cosmo
self.sigma8 = cosmo.sigma8()
return
def getDl( pii1=0.5e-10, pii2=1e-9, pri1=1e-13 ):
# Define your cosmology (what is not specified will be set to CLASS default parameters)
params = {
'output': 'tCl lCl pCl',
'modes': 's', # scalar perturbations
'lensing': 'yes',
'ic': 'ad&cdi',
'l_max_scalars':max_scalars,
'P_k_ini type': 'two_scales',
'k1': 0.002,
'k2': 0.1,
'P_{RR}^1': 2.34e-9,
'P_{RR}^2': 2.115e-9,
'P_{II}^1' : pii1,
'P_{II}^2' : pii2,
'P_{RI}^1' : pri1}
cosmo = Class()
cosmo.set(params)
cosmo.compute()
# print(dir(cosmo)) # use this command to see what is in the cosmo
# It is a dictionary that contains the fields: tt, te, ee, bb, pp, tp
cls = cosmo.raw_cl(max_l) # Access the cl until l=1000
yy = np.array( cls['ee'][1:] )
zz = np.array( cls['tt'][1:] )
yz = np.array( cls['te'][1:] )
ee = ((ell)*(ell+1) * yy / (2 * math.pi))
tt = ((ell)*(ell+1) * zz / (2 * math.pi))
te = ((ell)*(ell+1) * yz / (2 * math.pi))
cosmo.struct_cleanup()
return tt, te, ee
def calc_power_spec(cosmos, zs, lin=True):
Nc = len(cosmos)
Nz = len(zs)
ps = np.zeros((Nc, Nz, len(k)))
for i in range(Nc):
obh2, och2, w, ns, ln10As, H0, Neff, s8 = cosmos[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': 5.
}
if not lin:
params['non linear'] = 'halofit'
cosmo = Class()
cosmo.set(params)
cosmo.compute()
for j in range(len(zs)):
if lin:
ps[i, j] = np.array([cosmo.pk_lin(ki, zs[j]) for ki in k])
else:
ps[i, j] = np.array([cosmo.pk(ki, zs[j]) for ki in k])
continue
print("Finished box%d" % i)
return ps
def _run_class(self, **kwargs):
"""Method to run class and return the lensed and unlensed spectra as
dictionaries.
Returns
-------
cls_l : `dict`
Dictionary containing the lensed spectra for a given run of CLASS.
cls_u : `dict`
Dictionary containing the unlensed spectra for the same run of
CLASS.
"""
cosmo = Class()
# Set some parameters we want that are not in the default CLASS setting.
class_pars = {
'output': 'tCl pCl lCl',
'modes': 's, t',
'lensing': self.lensing,
'r': self.r,
}
# Update CLASS run with any kwargs that were passed. This is useful in
# Pyranha.compute_cosmology in order to compute the r=1 case.
class_pars.update(kwargs)
cosmo.set(class_pars)
cosmo.compute()
# Get the lensed and unlensed spectra as dictionaries.
cls_l = cosmo.lensed_cl(2500)
cls_u = cosmo.raw_cl(2500)
# Do the memory cleanup.
cosmo.struct_cleanup()
cosmo.empty()
return cls_l, cls_u
def get_power(params, l_min, l_max):
#CLASS gives results in natural units
#convert to muK^2 to match data
T_cmb = 2.7255e6 #temp in microkelvins
#create an instance of CLASS wrapper w/correct params
cosmo = Class()
cosmo.set(params)
#cosmo.set({'output':'tCl,pCl,lCl,mPk','lensing':'yes','P_k_max_1/Mpc':3.0})
cosmo.compute()
#lensed cl until l=l_max
output = cosmo.raw_cl(l_max) #lensed_cl(l_max)
ls = output['ell'][l_min:]
Cls = output['tt'][l_min:]
Dls = ls * (ls + 1) * Cls * T_cmb**2 / (2 * np.pi)
#clean ups
cosmo.struct_cleanup()
cosmo.empty()
return ls, Cls, Dls
def ComputeTransferData(settings, redshift):
database_key = settings.copy()
database_key.update({'redshift': tuple(redshift)})
database = Database.Database(config.DATABASE_DIR)
if database_key in database:
return database[database_key], redshift
else:
cosmo = Class()
cosmo.set(settings)
cosmo.compute()
outputData = [cosmo.get_transfer(z) for z in redshift]
# Calculate d_g/4+psi
for transfer_function_dict in outputData:
transfer_function_dict["d_g/4 + psi"] = transfer_function_dict[
"d_g"] / 4 + transfer_function_dict["psi"]
# Now filter the relevant fields
fields = TRANSFER_QUANTITIES + ["k (h/Mpc)"]
outputData = [{field: outputData[i][field]
for field in fields} for i in range(len(redshift))]
database[database_key] = outputData
return outputData, redshift
def compute_Sigma8(self):
# compute the values of sigma_8
# given that we have already assigned
# the cosmologies
# this part does not depend on the systematics
cosmo = Class()
cosmo.set(self.cosmoParams)
if self.settings.include_neutrino:
cosmo.set(self.other_settings)
cosmo.set(self.neutrino_settings)
cosmo.set(self.class_argumets)
try:
cosmo.compute()
except CosmoComputationError as failure_message:
print(failure_message)
self.sigma_8 = np.nan
except CosmoSevereError as critical_message:
print(critical_message)
self.sigma_8 = np.nan
sigma_8 = cosmo.sigma8()
cosmo.struct_cleanup()
cosmo.empty()
del cosmo
return sigma_8
from classy import Class
# In[ ]:
font = {'size' : 20, 'family':'STIXGeneral'}
axislabelfontsize='large'
matplotlib.rc('font', **font)
matplotlib.mathtext.rcParams['legend.fontsize']='medium'
# In[ ]:
#Lambda CDM
LCDM = Class()
LCDM.set({'Omega_cdm':0.25,'Omega_b':0.05})
LCDM.compute()
# In[ ]:
#Einstein-de Sitter
CDM = Class()
CDM.set({'Omega_cdm':0.95,'Omega_b':0.05})
CDM.compute()
# Just to cross-check that Omega_Lambda is negligible
# (but not exactly zero because we neglected radiation)
derived = CDM.get_current_derived_parameters(['Omega0_lambda'])
print derived
print "Omega_Lambda =",derived['Omega0_lambda']
class TestClass(unittest.TestCase):
"""
Testing Class and its wrapper classy on different cosmologies
To run it, do
~] nosetest test_class.py
It will run many times Class, on different cosmological scenarios, and
everytime testing for different output possibilities (none asked, only mPk,
etc..)
"""
def setUp(self):
"""
set up data used in the tests.
setUp is called before each test function execution.
"""
self.cosmo = 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 = {"lensing": "yes"}
def tearDown(self):
self.cosmo.struct_cleanup()
self.cosmo.empty()
del self.scenario
@parameterized.expand(
itertools.product(
("LCDM", "Mnu", "Positive_Omega_k", "Negative_Omega_k", "Isocurvature_modes"),
(
{"output": ""},
{"output": "mPk"},
{"output": "tCl"},
{"output": "tCl pCl lCl"},
{"output": "mPk tCl lCl", "P_k_max_h/Mpc": 10},
{"output": "nCl sCl"},
{"output": "tCl pCl lCl nCl sCl"},
),
({"gauge": "newtonian"}, {"gauge": "sync"}),
({}, {"non linear": "halofit"}),
)
)
def test_wrapper_implementation(self, name, scenario, gauge, nonlinear):
"""Create a few instances based on different cosmologies"""
if name == "Mnu":
self.scenario.update({"N_ncdm": 1, "m_ncdm": 0.06})
elif name == "Positive_Omega_k":
self.scenario.update({"Omega_k": 0.01})
elif name == "Negative_Omega_k":
self.scenario.update({"Omega_k": -0.01})
elif name == "Isocurvature_modes":
self.scenario.update({"ic": "ad,nid,cdi", "c_ad_cdi": -0.5})
self.scenario.update(scenario)
if scenario != {}:
self.scenario.update(gauge)
self.scenario.update(nonlinear)
sys.stderr.write("\n\n---------------------------------\n")
sys.stderr.write("| Test case %s |\n" % name)
sys.stderr.write("---------------------------------\n")
for key, value in self.scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
setting = self.cosmo.set(dict(self.verbose.items() + self.scenario.items()))
self.assertTrue(setting, "Class failed to initialize with input dict")
cl_list = ["tCl", "lCl", "pCl", "nCl", "sCl"]
# Depending on the cases, the compute should fail or not
should_fail = True
output = self.scenario["output"].split()
for elem in output:
if elem in ["tCl", "pCl"]:
for elem2 in output:
if elem2 == "lCl":
should_fail = False
break
if not should_fail:
self.cosmo.compute()
else:
self.assertRaises(CosmoSevereError, self.cosmo.compute)
return
self.assertTrue(self.cosmo.state, "Class failed to go through all __init__ methods")
if self.cosmo.state:
print "--> Class is ready"
# Depending
if "output" in self.scenario.keys():
# Positive tests
output = self.scenario["output"]
for elem in output.split():
if elem in cl_list:
print "--> testing raw_cl function"
cl = self.cosmo.raw_cl(100)
self.assertIsNotNone(cl, "raw_cl returned nothing")
self.assertEqual(np.shape(cl["tt"])[0], 101, "raw_cl returned wrong size")
if elem == "mPk":
print "--> testing pk function"
pk = self.cosmo.pk(0.1, 0)
self.assertIsNotNone(pk, "pk returned nothing")
# Negative tests of output functions
if not any([elem in cl_list for elem in output.split()]):
print "--> testing absence of any Cl"
self.assertRaises(CosmoSevereError, self.cosmo.raw_cl, 100)
if "mPk" not in self.scenario["output"].split():
print "--> testing absence of mPk"
# args = (0.1, 0)
self.assertRaises(CosmoSevereError, self.cosmo.pk, 0.1, 0)
@parameterized.expand(
itertools.product(("massless", "massive", "both"), ("photons", "massless", "exact"), ("t", "s, t"))
)
def test_tensors(self, scenario, method, modes):
"""Test the new tensor mode implementation"""
self.scenario = {}
if scenario == "massless":
self.scenario.update({"N_eff": 3.046, "N_ncdm": 0})
elif scenario == "massiv":
self.scenario.update({"N_eff": 0, "N_ncdm": 2, "m_ncdm": "0.03, 0.04", "deg_ncdm": "2, 1"})
elif scenario == "both":
self.scenario.update({"N_eff": 1.5, "N_ncdm": 2, "m_ncdm": "0.03, 0.04", "deg_ncdm": "1, 0.5"})
sys.stderr.write("\n\n---------------------------------\n")
sys.stderr.write("| Test case: %s %s %s |\n" % (scenario, method, modes))
sys.stderr.write("---------------------------------\n")
self.scenario.update({"tensor method": method, "modes": modes, "output": "tCl, pCl"})
for key, value in self.scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
self.cosmo.set(dict(self.verbose.items() + self.scenario.items()))
self.cosmo.compute()
@parameterized.expand(itertools.izip(powerset(["100*theta_s", "Omega_dcdmdr"]), powerset([1.04, 0.20])))
def test_shooting_method(self, variables, values):
Omega_cdm = 0.25
scenario = {"Omega_b": 0.05}
for variable, value in zip(variables, values):
scenario.update({variable: value})
if "Omega_dcdmdr" in variables:
scenario.update({"Gamma_dcdm": 100, "Omega_cdm": Omega_cdm - scenario["Omega_dcdmdr"]})
else:
scenario.update({"Omega_cdm": Omega_cdm})
sys.stderr.write("\n\n---------------------------------\n")
sys.stderr.write("| Test shooting: %s |\n" % (", ".join(variables)))
sys.stderr.write("---------------------------------\n")
for key, value in scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
scenario.update(self.verbose)
self.assertTrue(self.cosmo.set(scenario), "Class failed to initialise with this input")
self.assertRaises
self.cosmo.compute()
# Now, check that the values are properly extracted
for variable, value in zip(variables, values):
if variable == "100*theta_s":
computed_value = self.cosmo.get_current_derived_parameters([variable])[variable]
self.assertAlmostEqual(value, computed_value, places=5)
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {#'As':'A_s',
#'ns':'n_s',
#'r':'r',
'custom1':'custom1',
'custom2':'custom2',
'custom3':'custom3',
#'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
'omk':'Omega_k',
'l_max_scalar':'l_max_scalars',
'l_max_tensor':'l_max_tensors',
'Tcmb':'T_cmb'
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
#def __call__(self,
# **kwargs):
# d={}
# for k, v in kwargs.iteritems():
# if k in self.name_mapping and v is not None:
# d[self.name_mapping[k]]=v
# else:
# d[k]=v
#def __call__(self,
#ombh2,
#omch2,
#H0,
#As,
#ns,
#custom1,
#custom2,
#custom3,
#tau,
#w=None,
#r=None,
#nrun=None,
#omk=0,
#Yp=None,
#Tcmb=2.7255,
#massless_neutrinos=3.046,
#l_max_scalar=3000,
#l_max_tensor=3000,
#pivot_scalar=0.05,
#outputs=[],
#**kwargs):
#print kwargs
def __call__(self,**kwargs):
#print kwargs
#print kwargs['classparamlist']
#print kwargs['d']
d={}
for k,v in kwargs.iteritems():
if k in kwargs['classparamlist']:
if k in self.name_mapping and v is not None:
d[self.name_mapping[k]]=v
else:
d[k]=v
#d['P_k_ini type']='external_Pk'
#d['modes'] = 's,t'
self.model.set(**d)
l_max = d['l_max_scalars']
Tcmb = d['T_cmb']
#print l_max
#print d
self.model.compute()
ell = arange(l_max+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill
"""
#{cosmoslik name : class name}
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot'}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
tau,
omnuh2, #0.006
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
massive_neutrinos=1,
massless_neutrinos=2.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.002,
outputs=[],
**kwargs):
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
**{self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None})
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
params = {
'output': 'tCl lCl',
'l_max_scalars': 2508,
'lensing': 'yes',
'P_k_ini type': 'external_Pk',
'command': 'python /home/andrew/Research/tools/class_public-2.4.3/external_Pk/generate_Pk_cosines.py',
'custom1': 0,
'custom2': 0,
'custom3': 0,
'custom4': 0,
'custom5': 0}
#Get the unperturbed cls for comparison
cosmo = Class()
cosmo.set(params)
cosmo.compute()
clso=cosmo.lensed_cl(2508)['tt'][30:]
ell = cosmo.lensed_cl(2508)['ell'][30:]
for i in range(len(clso)):
clso[i]=ell[i]*(ell[i]+1)/(4*np.pi)*((2.726e6)**2)*clso[i]
a=np.zeros(5)
cosmo.struct_cleanup()
cosmo.empty()
dcls=np.zeros([clso.shape[0],5])
h=1e-6
for m in range(5):
a[m]=h
# Define your cosmology (what is not specified will be set to CLASS default parameters)
params = {
#'k_step_sub':'0.01',
'k_per_decade_for_pk':k_per_decade,
'k_per_decade_for_bao':k_per_decade,
'k_min_tau0':k_min_tau0, # this value controls the minimum k value in the figure
'perturb_sampling_stepsize':'0.05',
'P_k_max_1/Mpc':P_k_max_inv_Mpc,
'compute damping scale':'yes', # needed to output and plot Silk damping scale
'gauge':'newtonian'}
###############
#
# call CLASS
#
###############
M = Class()
M.set(common_settings)
M.compute()
#
# define conformal time sampling array
#
times = M.get_current_derived_parameters(['tau_rec','conformal_age'])
tau_rec=times['tau_rec']
tau_0 = times['conformal_age']
tau1 = np.logspace(math.log10(tau_ini),math.log10(tau_rec),tau_num_early)
tau2 = np.logspace(math.log10(tau_rec),math.log10(tau_0),tau_num_late)[1:]
tau2[-1] *= 0.999 # this tiny shift avoids interpolation errors
tau = np.concatenate((tau1,tau2))
tau_num = len(tau)
#
# use table of background and thermodynamics quantitites to define some functions
# returning some characteristic scales
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'k_c':'k_c',
'alpha_exp':'alpha_exp',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
#'Tcmb':'T_cmb',
#'P_k_max_hinvMpc':'P_k_max_h/Mpc'
#'w':'w0_fld',
#'nrun':'alpha_s',
#'omk':'Omega_k',
#'l_max_scalar':'l_max_scalars',
#'l_max_tensor':'l_max_tensors'
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
k_c,
alpha_exp,
tau,
#omnuh2=0, #0.006 #None means that Class will take the default for this, maybe?
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
#massive_neutrinos=0,
massless_neutrinos=3.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.05,
outputs=[],
**kwargs):
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
**{self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None})
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'phi0':'custom1',
'm6':'custom2',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
phi0,
m6,
tau,
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
massless_neutrinos=3.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.05,
outputs=[],
**kwargs):
d={self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None}
d['P_k_ini type']='external_Pk'
d['modes'] = 's,t'
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
command = '../LSODAtesnors/pk',
**d)
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class TestClass(unittest.TestCase):
"""
Testing Class and its wrapper classy on different cosmologies
To run it, do
~] nosetest test_class.py
It will run many times Class, on different cosmological scenarios, and
everytime testing for different output possibilities (none asked, only mPk,
etc..)
"""
def setUp(self):
"""
set up data used in the tests.
setUp is called before each test function execution.
"""
self.cosmo = Class()
self.verbose = {
'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 = {'lensing':'yes'}
def tearDown(self):
self.cosmo.struct_cleanup()
self.cosmo.empty()
del self.scenario
@parameterized.expand(
itertools.product(
('LCDM',
'Mnu',
'Positive_Omega_k',
'Negative_Omega_k',
'Isocurvature_modes', ),
({'output': ''}, {'output': 'mPk'}, {'output': 'tCl'},
{'output': 'tCl pCl lCl'}, {'output': 'mPk tCl lCl', 'P_k_max_h/Mpc':10},
{'output': 'nCl sCl'}, {'output': 'tCl pCl lCl nCl sCl'}),
({'gauge': 'newtonian'}, {'gauge': 'sync'}),
({}, {'non linear': 'halofit'})))
def test_parameters(self, name, scenario, gauge, nonlinear):
"""Create a few instances based on different cosmologies"""
if name == 'Mnu':
self.scenario.update({'N_ncdm': 1, 'm_ncdm': 0.06})
elif name == 'Positive_Omega_k':
self.scenario.update({'Omega_k': 0.01})
elif name == 'Negative_Omega_k':
self.scenario.update({'Omega_k': -0.01})
elif name == 'Isocurvature_modes':
self.scenario.update({'ic': 'ad,nid,cdi', 'c_ad_cdi': -0.5})
self.scenario.update(scenario)
if scenario != {}:
self.scenario.update(gauge)
self.scenario.update(nonlinear)
sys.stderr.write('\n\n---------------------------------\n')
sys.stderr.write('| Test case %s |\n' % name)
sys.stderr.write('---------------------------------\n')
for key, value in self.scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
setting = self.cosmo.set(
dict(self.verbose.items()+self.scenario.items()))
self.assertTrue(setting, "Class failed to initialize with input dict")
cl_list = ['tCl', 'lCl', 'pCl', 'nCl', 'sCl']
# Depending on the cases, the compute should fail or not
should_fail = True
output = self.scenario['output'].split()
for elem in output:
if elem in ['tCl', 'pCl']:
for elem2 in output:
if elem2 == 'lCl':
should_fail = False
break
if not should_fail:
self.cosmo.compute()
else:
self.assertRaises(CosmoSevereError, self.cosmo.compute)
return
self.assertTrue(
self.cosmo.state,
"Class failed to go through all __init__ methods")
if self.cosmo.state:
print '--> Class is ready'
# Depending
if 'output' in self.scenario.keys():
# Positive tests
output = self.scenario['output']
for elem in output.split():
if elem in cl_list:
print '--> testing raw_cl function'
cl = self.cosmo.raw_cl(100)
self.assertIsNotNone(cl, "raw_cl returned nothing")
self.assertEqual(
np.shape(cl['tt'])[0], 101,
"raw_cl returned wrong size")
if elem == 'mPk':
print '--> testing pk function'
pk = self.cosmo.pk(0.1, 0)
self.assertIsNotNone(pk, "pk returned nothing")
# Negative tests of output functions
if not any([elem in cl_list for elem in output.split()]):
print '--> testing absence of any Cl'
self.assertRaises(CosmoSevereError, self.cosmo.raw_cl, 100)
if 'mPk' not in self.scenario['output'].split():
print '--> testing absence of mPk'
#args = (0.1, 0)
self.assertRaises(CosmoSevereError, self.cosmo.pk, 0.1, 0)
print '~~~~~~~~ passed ? '
@parameterized.expand(
itertools.product(
('massless', 'massive', 'both'),
('photons', 'massless', 'exact'),
('t', 's, t')))
def test_tensors(self, scenario, method, modes):
"""Test the new tensor mode implementation"""
self.scenario = {}
if scenario == 'massless':
self.scenario.update({'N_eff': 3.046, 'N_ncdm':0})
elif scenario == 'massiv':
self.scenario.update(
{'N_eff': 0, 'N_ncdm': 2, 'm_ncdm': '0.03, 0.04',
'deg_ncdm': '2, 1'})
elif scenario == 'both':
self.scenario.update(
{'N_eff': 1.5, 'N_ncdm': 2, 'm_ncdm': '0.03, 0.04',
'deg_ncdm': '1, 0.5'})
self.scenario.update({
'tensor method': method, 'modes': modes,
'output': 'tCl, pCl'})
for key, value in self.scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
self.cosmo.set(
dict(self.verbose.items()+self.scenario.items()))
self.cosmo.compute()
'h':0.67556,
'omega_b':0.022032,
'omega_cdm':0.12038,
'A_s':2.215e-9,
'n_s':0.9619,
'tau_reio':0.0925,
# Take fixed value for primordial Helium (instead of automatic BBN adjustment)
'YHe':0.246,
# other output and precision parameters
'l_max_scalars':5000}
###############
#
# call CLASS
#
M = Class()
M.set(common_settings)
M.compute()
cl_tot = M.raw_cl(3000)
cl_lensed = M.lensed_cl(3000)
M.struct_cleanup() # clean output
M.empty() # clean input
#
M.set(common_settings) # new input
M.set({'temperature contributions':'tsw'})
M.compute()
cl_tsw = M.raw_cl(3000)
M.struct_cleanup()
M.empty()
#
M.set(common_settings)
M.set({'temperature contributions':'eisw'})
if False: # In case you want to plot the N(z)s fig, axs = plt.subplots(1, 2, figsize=(14, 5)) finer_z_grid = np.linspace(0, 2, num=2000) for i, nz in enumerate(redshiftdistributions): ic = str(i+1) nz_grid = nz.eval(z_grid) finer_nz_grid = nz.eval(finer_z_grid) axs[i].plot(finer_z_grid, finer_nz_grid, label='Gaussian Mixture') axs[i].plot(z_grid, nz_grid, label='Histogram-ized', ls='steps') plt.show() # Now run Class! cosmo = Class() # Scenario 1 cosmo.set(dict(mainparams.items()+scenario1.items())) cosmo.compute() cl1 = cosmo.density_cl(mainparams['l_max_lss']) cosmo.struct_cleanup() cosmo.empty() # Scenario 2 cosmo.set(dict(mainparams.items()+scenario2.items())) cosmo.compute() cl2 = cosmo.density_cl(mainparams['l_max_lss']) cosmo.struct_cleanup() cosmo.empty() # The Cls should be very close if the histogram is binned finely nbins = len(redshiftdistributions) print 'Comparing accuracy of N(z) representation: multigaussian vs histograms' for i in range(nbins*(nbins+1)/2):
'ncdm_fluid_approximation':3,
# You may uncomment this line to get more info on the ncdm sector from Class:
'background_verbose':1
}
# array of k values in 1/Mpc
kvec = np.logspace(-4,np.log10(3),100)
# array for storing legend
legarray = []
# loop over total mass values
for sum_masses in [0.1, 0.115, 0.13]:
# normal hierarchy
[m1, m2, m3] = get_masses(2.45e-3,7.50e-5, sum_masses, 'NH')
NH = Class()
NH.set(commonsettings)
NH.set({'m_ncdm':str(m1)+','+str(m2)+','+str(m3)})
NH.compute()
# inverted hierarchy
[m1, m2, m3] = get_masses(2.45e-3,7.50e-5, sum_masses, 'IH')
IH = Class()
IH.set(commonsettings)
IH.set({'m_ncdm':str(m1)+','+str(m2)+','+str(m3)})
IH.compute()
pkNH = []
pkIH = []
for k in kvec:
pkNH.append(NH.pk(k,0.))
pkIH.append(IH.pk(k,0.))
NH.struct_cleanup()
IH.struct_cleanup()
# coding: utf-8
# In[ ]:
# import classy module
from classy import Class
# In[ ]:
# create instance of the class "Class"
LambdaCDM = Class()
# pass input parameters
LambdaCDM.set({'omega_b':0.022032,'omega_cdm':0.12038,'h':0.67556,'A_s':2.215e-9,'n_s':0.9619,'tau_reio':0.0925})
LambdaCDM.set({'output':'tCl,pCl,lCl,mPk','lensing':'yes','P_k_max_1/Mpc':3.0})
# run class
LambdaCDM.compute()
# In[ ]:
# get all C_l output
cls = LambdaCDM.lensed_cl(2500)
# To check the format of cls
cls.viewkeys()
# In[ ]:
ll = cls['ell'][2:]
class Model():
def __init__(self, cosmo=None):
"""
Initialize the Model class. By default Model uses its own Class
instance.
cosmo = external Class instance. Default is None
"""
if cosmo:
self.cosmo = cosmo
else:
self.cosmo = Class()
self.computed = {}
self.texnames = {}
def __set_scale(self, axes, xscale, yscale):
"""
Set scales for axes in axes array.
axes = axes array (e.g. f, ax = plt.subplots(2,2))
xscale = linear array of xscale.
yscale = linear array of yscale.
Scales are set once axes is flatten. Each plot is counted from left to
right an from top to bottom.
"""
for i, ax in enumerate(axes.flat):
ax.set_xscale(xscale[i])
ax.set_yscale(yscale[i])
def __set_label(self, axes, xlabel, ylabel):
"""
Set labels for axes in axes array.
axes = axes array (e.g. f, ax = plt.subplots(2,2))
xlabel = linear array of xlabels.
ylabel = linear array of ylabels.
Labels are set once axes is flatten. Each plot is counted from left to
right an from top to bottom.
"""
for i, ax in enumerate(axes.flat):
ax.set_xlabel(xlabel[i])
ax.set_ylabel(ylabel[i])
def __store_cl(self, cl_dic):
"""
Store cl's as (l*(l+1)/2pi)*cl, which is much more useful.
"""
ell = cl_dic['ell'][2:]
for cl, list_val in cl_dic.iteritems():
list_val = list_val[2:]
if (list_val == ell).all():
cl_dic[cl] = list_val
continue
list_val = (ell * (ell + 1) / (2 * np.pi)) * list_val
cl_dic[cl] = list_val # Remove first two null items (l=0,1)
return cl_dic
def add_derived(self, varied_name, keys, value):
"""
Add a derived parameter for varied_name dictionary.
varied_name = varied variable's name.
keys = list of keys in descending level.
value = value to store for new dictionary key.
"""
dic = self.computed[varied_name]
for key in keys:
if key not in dic:
dic[key] = {}
dic = dic[key]
dic.update(value)
def compute_models(self, params, varied_name, index_variable, values,
back=[], thermo=[], prim=[], pert=[], trans=[],
pk=[0.0001, 0.1, 100], extra=[], update=True,
cosmo_msg=False, texname=""):
"""
Fill dic with the hi_class output structures for the model with given
params, modifying the varied_name value with values.
params = parameters to be set in Class. They must be in agreement with
what is asked for.
varied_name = the name of the variable you are modifying. It will be
used as key in dic assigned to its background structures.
index_variable = variable's index in parameters_smg array.
values = varied variable values you want to compute the cosmology for.
back = list of variables to store from background. If 'all', store the
whole dictionary.
thermo = list of variables to store from thermodynamics. If 'all',
store the whole dictionary.
prim = list of variables to store from primordial. If 'all', store the
whole dictionary.
pert = list of variables to store from perturbations. If 'all', store
the whole dictionary.
trans = list of variables to store from transfer. If 'all', store
the whole dictionary. get_transfer accept two optional
arguments: z=0 and output_format='class' (avaible options are
'class' or 'camb'). If different values are desired, first
item of trans must be {'z': value, 'output_format': value}.
pk = list with the minimum and maximum k values to store the present
matter power spectrum and the number of points [k_min, k_max,
number_points]. Default [10^-4, 10^1, 100].
extra = list of any of the method or objects defined in cosmo, e.g.
w0_smg(). It will store {'method': cosmo.w0_smg()}
update = if True update old computed[key] dictionary elsewise create a
new one. Default: True.
cosmo_msg = if True, print cosmo.compute() messages. Default: False.
"""
key = varied_name
if texname:
self.set_texnames({varied_name: texname})
elif key not in self.texnames: # texname will not be set at this stage. No check required
self.set_texnames({varied_name: varied_name})
if (not update) or (key not in self.computed.keys()):
self.computed[key] = od()
for val in values:
# key = "{}={}".format(varied_name, val)
params["parameters_smg"] = inip.vary_params(params["parameters_smg"], [[index_variable, val]])
# It might be after the try to not store empty dictionaries.
# Nevertheless, I find more useful having them to keep track of
# those failed and, perhaps, to implement a method to obtain them
# with Omega_smg_debug.
d = self.computed[key][val] = {}
self.cosmo.empty()
self.cosmo.set(params)
try:
self.cosmo.compute()
except Exception, e:
print "Error: skipping {}={}".format(key, val)
if cosmo_msg:
print e
continue
d['tunned'] = self.cosmo.get_current_derived_parameters(['tuning_parameter'])['tuning_parameter']
for lst in [[back, 'back', self.cosmo.get_background],
[thermo, 'thermo', self.cosmo.get_thermodynamics],
[prim, 'prim', self.cosmo.get_thermodynamics]]:
if lst[0]:
output = lst[2]()
if lst[0][0] == 'all':
d[lst[1]] = output
else:
d[lst[1]] = {}
for item in back:
if type(item) is list:
d[lst[1]].update({item[0]: output[item[0]][item[1]]})
else:
d[lst[1]].update({item: output[item]})
if pert:
# Perturbation is tricky because it can accept two optional
# argument for get_perturbations and this method returns a
# dictionary {'kind_of_pert': [{variable: list_values}]}, where
# each item in the list is for a k (chosen in params).
if type(pert[0]) is dict:
output = self.cosmo.get_perturbations(pert[0]['z'], pert[0]['output_format'])
if pert[1] == 'all':
d['pert'] = output
else:
output = self.cosmo.get_perturbations()
if pert[0] == 'all':
d['pert'] = output
if (type(pert[0]) is not dict) and (pert[0] != 'all'):
d['pert'] = {}
for subkey, lst in output.iteritems():
d['pert'].update({subkey: []})
for n, kdic in enumerate(lst): # Each item is for a k
d['pert'][subkey].append({})
for item in pert:
if type(item) is list:
d['pert'][subkey][n].update({item[0]: kdic[item[0]][item[1]]})
else:
d['pert'][subkey][n].update({item: kdic[item]})
for i in extra:
exec('d[i] = self.cosmo.{}'.format(i))
try:
d['cl'] = self.__store_cl(self.cosmo.raw_cl())
except CosmoSevereError:
pass
try:
d['lcl'] = self.__store_cl(self.cosmo.lensed_cl())
except CosmoSevereError:
pass
try:
d['dcl'] = self.cosmo.density_cl()
except CosmoSevereError:
pass
if ("output" in self.cosmo.pars) and ('mPk' in self.cosmo.pars['output']):
k_array = np.linspace(*pk)
pk_array = np.array([self.cosmo.pk(k, 0) for k in k_array])
d['pk'] = {'k': k_array, 'pk': pk_array}
self.cosmo.struct_cleanup()
class TestClass(unittest.TestCase):
"""
Testing Class and its wrapper classy on different cosmologies
To run it, do
~] nosetest test_class.py
It will run many times Class, on different cosmological scenarios, and
everytime testing for different output possibilities (none asked, only mPk,
etc..)
"""
@classmethod
def setUpClass(self):
self.faulty_figs_path = os.path.join(
os.path.sep.join(os.path.realpath(__file__).split(os.path.sep)[:-1]), "faulty_figs"
)
if os.path.isdir(self.faulty_figs_path):
shutil.rmtree(self.faulty_figs_path)
os.mkdir(self.faulty_figs_path)
@classmethod
def tearDownClass(self):
pass
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 tearDown(self):
self.cosmo.struct_cleanup()
self.cosmo.empty()
self.cosmo_newt.struct_cleanup()
self.cosmo_newt.empty()
del self.scenario
def poormansname(self, somedict):
string = "_".join([k + "=" + str(v) for k, v in somedict.iteritems()])
string = string.replace("/", "%")
string = string.replace(",", "")
string = string.replace(" ", "")
return string
@parameterized.expand(TUPLE_ARRAY)
def test_0wrapper_implementation(self, inputdict):
"""Create a few instances based on different cosmologies"""
self.scenario.update(inputdict)
self.name = self.poormansname(inputdict)
sys.stderr.write("\n\n---------------------------------\n")
sys.stderr.write("| Test case %s |\n" % self.name)
sys.stderr.write("---------------------------------\n")
for key, value in self.scenario.iteritems():
sys.stderr.write("%s = %s\n" % (key, value))
sys.stdout.write("%s = %s\n" % (key, value))
sys.stderr.write("\n")
setting = self.cosmo.set(dict(self.verbose.items() + self.scenario.items()))
self.assertTrue(setting, "Class failed to initialize with input dict")
cl_dict = {"tCl": ["tt"], "lCl": ["pp"], "pCl": ["ee", "bb"]}
density_cl_list = ["nCl", "sCl"]
# 'lensing' is always set to yes. Therefore, trying to compute 'tCl' or
# 'pCl' will fail except if we also ask for 'lCl'. The flag
# 'should_fail' stores this status.
sys.stderr.write("Should")
should_fail = self.test_incompatible_input()
if should_fail:
sys.stderr.write(" fail...\n")
else:
sys.stderr.write(" not fail...\n")
if not should_fail:
self.cosmo.compute()
else:
self.assertRaises(CosmoSevereError, self.cosmo.compute)
return
self.assertTrue(self.cosmo.state, "Class failed to go through all __init__ methods")
if self.cosmo.state:
print "--> Class is ready"
# Depending
if "output" in self.scenario.keys():
# Positive tests of raw cls
output = self.scenario["output"]
for elem in output.split():
if elem in cl_dict.keys():
for cl_type in cl_dict[elem]:
sys.stderr.write("--> testing raw_cl for %s\n" % cl_type)
cl = self.cosmo.raw_cl(100)
self.assertIsNotNone(cl, "raw_cl returned nothing")
self.assertEqual(np.shape(cl[cl_type])[0], 101, "raw_cl returned wrong size")
# TODO do the same for lensed if 'lCl' is there, and for
# density cl
if elem == "mPk":
sys.stderr.write("--> testing pk function\n")
pk = self.cosmo.pk(0.1, 0)
self.assertIsNotNone(pk, "pk returned nothing")
# Negative tests of output functions
if not any([elem in cl_dict.keys() for elem in output.split()]):
sys.stderr.write("--> testing absence of any Cl\n")
self.assertRaises(CosmoSevereError, self.cosmo.raw_cl, 100)
if "mPk" not in output.split():
sys.stderr.write("--> testing absence of mPk\n")
self.assertRaises(CosmoSevereError, self.cosmo.pk, 0.1, 0)
if COMPARE_OUTPUT:
# Now, compute with Newtonian gauge, and compare the results
self.cosmo_newt.set(dict(self.verbose.items() + self.scenario.items()))
self.cosmo_newt.set({"gauge": "newtonian"})
self.cosmo_newt.compute()
# Check that the computation worked
self.assertTrue(self.cosmo_newt.state, "Class failed to go through all __init__ methods in Newtonian gauge")
self.compare_output(self.cosmo, self.cosmo_newt)
def test_incompatible_input(self):
should_fail = False
# If we have tensor modes, we must have one tensor observable,
# either tCl or pCl.
if has_tensor(self.scenario):
if "output" not in self.scenario.keys():
should_fail = True
else:
output = self.scenario["output"].split()
if "tCl" not in output and "pCl" not in output:
should_fail = True
# If we have specified lensing, we must have lCl in output,
# otherwise lensing will not be read (which is an error).
if "lensing" in self.scenario.keys():
if "output" not in self.scenario.keys():
should_fail = True
else:
output = self.scenario["output"].split()
if "lCl" not in output:
should_fail = True
elif "tCl" not in output and "pCl" not in output:
should_fail = True
# If we have specified a tensor method, we must have tensors.
if "tensor method" in self.scenario.keys():
if not has_tensor(self.scenario):
should_fail = True
# If we have specified non linear, we must have some form of
# perturbations output.
if "non linear" in self.scenario.keys():
if "output" not in self.scenario.keys():
should_fail = True
# If we ask for Cl's of lensing potential, we must have scalar modes.
if "output" in self.scenario.keys() and "lCl" in self.scenario["output"].split():
if "modes" in self.scenario.keys() and self.scenario["modes"].find("s") == -1:
should_fail = True
# If we specify initial conditions (for scalar modes), we must have
# perturbations and scalar modes.
if "ic" in self.scenario.keys():
if "modes" in self.scenario.keys() and self.scenario["modes"].find("s") == -1:
should_fail = True
if "output" not in self.scenario.keys():
should_fail = True
# If we use inflation module, we must have scalar modes,
# tensor modes, no vector modes and we should only have adiabatic IC:
if "P_k_ini type" in self.scenario.keys() and self.scenario["P_k_ini type"].find("inflation") != -1:
if "modes" not in self.scenario.keys():
should_fail = True
else:
if self.scenario["modes"].find("s") == -1:
should_fail = True
if self.scenario["modes"].find("v") != -1:
should_fail = True
if self.scenario["modes"].find("t") == -1:
should_fail = True
if "ic" in self.scenario.keys() and self.scenario["ic"].find("i") != -1:
should_fail = True
return should_fail
def compare_output(self, reference, candidate):
sys.stderr.write("\n\n---------------------------------\n")
sys.stderr.write("| Comparing synch and Newt: |\n")
sys.stderr.write("---------------------------------\n")
for elem in ["raw_cl", "lensed_cl", "density_cl"]:
# Try to get the elem, but if they were not computed, a
# CosmoComputeError should be raised. In this case, ignore the
# whole block.
try:
to_test = getattr(candidate, elem)()
except CosmoSevereError:
continue
ref = getattr(reference, elem)()
for key, value in ref.iteritems():
if key != "ell":
sys.stderr.write("--> testing equality of %s %s\n" % (elem, key))
# For all self spectra, try to compare allclose
if key[0] == key[1]:
# If it is a 'dd' or 'll', it is a dictionary.
if isinstance(value, dict):
for subkey in value.iterkeys():
try:
np.testing.assert_allclose(
value[subkey], to_test[key][subkey], rtol=1e-03, atol=1e-20
)
except AssertionError:
self.cl_faulty_plot(elem + "_" + key, value[subkey][2:], to_test[key][subkey][2:])
except TypeError:
self.cl_faulty_plot(elem + "_" + key, value[subkey][2:], to_test[key][subkey][2:])
else:
try:
np.testing.assert_allclose(value, to_test[key], rtol=1e-03, atol=1e-20)
except AssertionError:
self.cl_faulty_plot(elem + "_" + key, value[2:], to_test[key][2:])
except TypeError:
self.cl_faulty_plot(elem + "_" + key, value[2:], to_test[key][2:])
# For cross-spectra, as there can be zero-crossing, we
# instead compare the difference.
else:
# First, we multiply each array by the biggest value
norm = max(np.abs(value).max(), np.abs(to_test[key]).max())
value *= norm
to_test[key] *= norm
try:
np.testing.assert_array_almost_equal(value, to_test[key], decimal=3)
except AssertionError:
self.cl_faulty_plot(elem + "_" + key, value[2:], to_test[key][2:])
if "output" in self.scenario.keys():
if self.scenario["output"].find("mPk") != -1:
sys.stderr.write("--> testing equality of Pk")
k = np.logspace(-2, log10(self.scenario["P_k_max_1/Mpc"]))
reference_pk = np.array([reference.pk(elem, 0) for elem in k])
candidate_pk = np.array([candidate.pk(elem, 0) for elem in k])
try:
np.testing.assert_allclose(reference_pk, candidate_pk, rtol=5e-03, atol=1e-20)
except AssertionError:
self.pk_faulty_plot(k, reference_pk, candidate_pk)
def cl_faulty_plot(self, cl_type, reference, candidate):
path = os.path.join(self.faulty_figs_path, self.name)
fig = plt.figure()
ax_lin = plt.subplot(211)
ax_log = plt.subplot(212)
ell = np.arange(max(np.shape(candidate))) + 2
ax_lin.plot(ell, 1 - candidate / reference)
ax_log.loglog(ell, abs(1 - candidate / reference))
ax_lin.set_xlabel("l")
ax_log.set_xlabel("l")
ax_lin.set_ylabel("1-candidate/reference")
ax_log.set_ylabel("abs(1-candidate/reference)")
ax_lin.set_title(self.name)
ax_log.set_title(self.name)
ax_lin.legend([cl_type])
ax_log.legend([cl_type])
fig.savefig(path + "_" + cl_type + ".pdf")
# Store parameters (contained in self.scenario) to text file
parameters = dict(self.verbose.items() + self.scenario.items())
with open(path + ".ini", "w") as param_file:
for key, value in parameters.iteritems():
param_file.write(key + " = " + str(value) + "\n")
def pk_faulty_plot(self, k, reference, candidate):
path = os.path.join(self.faulty_figs_path, self.name)
fig = plt.figure()
ax_lin = plt.subplot(211)
ax_log = plt.subplot(212)
ax_lin.plot(k, 1 - candidate / reference)
ax_log.loglog(k, abs(1 - candidate / reference))
ax_lin.set_xlabel("k")
ax_log.set_xlabel("k")
ax_lin.set_ylabel("1-candidate/reference")
ax_log.set_ylabel("abs(1-candidate/reference)")
ax_lin.set_title(self.name)
ax_log.set_title(self.name)
ax_lin.legend("$P_k$")
ax_log.legend("$P_k$")
fig.savefig(path + "_" + "pk" + ".pdf")
# Store parameters (contained in self.scenario) to text file
parameters = dict(self.verbose.items() + self.scenario.items())
with open(path + ".ini", "w") as param_file:
for key, value in parameters.iteritems():
param_file.write(key + " = " + str(value) + "\n")