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 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 __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 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 __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 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"}
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 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 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
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 tsz_gal_cl:
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 = 100.
self.zmax = 4. # should be consistent with fortran code
self.nk_pk = 500
self.nz_pk = 51
# Class
self.cosmo = Class()
def get_tsz_cl(self,ell_arr,params,dndz,z1,z2,z1_ng,z2_ng,nm,nz):
self.zmin = z1
self.zmax = z2
obh2 = params['obh2']
och2 = params['och2']
As = params['As']
ns = params['ns']
mnu = params['mnu']
mass_bias = params['mass_bias']
Mcut = params['Mcut']
M1 = params['M1']
kappa = params['kappa']
sigma_Ncen = params['sigma_Ncen']
alp_Nsat = params['alp_Nsat']
rmax = params['rmax']
rgs = params['rgs']
flag_nu_logic = params['flag_nu']
flag_tll_logic = params['flag_tll']
if type(flag_nu_logic) != bool:
print('flag_nu must be boolean.')
sys.exit()
if flag_nu_logic:
flag_nu = 1
else:
flag_nu = 0
if type(flag_tll_logic) != bool:
print('flag_tll must be boolean.')
sys.exit()
if flag_tll_logic:
flag_tll = 1
else:
flag_tll = 0
if 'theta' in params.keys():
theta = params['theta']
pars = {'output':'mPk','100*theta_s':theta,
'omega_b':obh2,'omega_cdm':och2,
'A_s':As,'n_s':ns,\
'N_ur':0.00641,'N_ncdm':1,'m_ncdm':mnu/3.,\
'T_ncdm':0.71611,\
'P_k_max_h/Mpc': self.kmax,'z_max_pk':self.zmax,\
'deg_ncdm':3.}
self.cosmo.set(pars)
self.cosmo.compute()
h0 = self.cosmo.h()
elif 'h0' in params.keys():
h0 = params['h0']
pars = {'output':'mPk','h':h0,
'omega_b':obh2,'omega_cdm':och2,
'A_s':As,'n_s':ns,\
'N_ur':0.00641,'N_ncdm':1,'m_ncdm':mnu/3.,\
'T_ncdm':0.71611,\
'P_k_max_h/Mpc': self.kmax,'z_max_pk':self.zmax,\
'deg_ncdm':3.}
print(pars)
self.cosmo.set(pars)
self.cosmo.compute()
derived = self.cosmo.get_current_derived_parameters(['100*theta_s','sigma8'])
vz = (self.cosmo.angular_distance(z2_ng)**3*(1+z2_ng)**3 \
-self.cosmo.angular_distance(z1_ng)**3*(1+z1_ng)**3)
vz = vz*h0**3*4.*np.pi/3.
derived['vz'] = vz
# get matter power spectra
kh_arr = np.logspace(np.log10(self.kmin),np.log10(self.kmax),self.nk_pk)
kh = np.zeros((self.nz_pk,self.nk_pk))
pk = np.zeros((self.nz_pk,self.nk_pk))
pk_zarr = np.linspace(self.zmin,self.zmax,self.nz_pk)
for i in range(self.nz_pk):
kh[i,:] = kh_arr
if flag_nu == 0:
pk[i,:] = np.array([self.cosmo.pk(k*h0,pk_zarr[i])*h0**3 for k in kh_arr])
elif flag_nu == 1:
pk[i,:] = np.array([self.cosmo.pk_cb(k*h0,pk_zarr[i])*h0**3 for k in kh_arr])
# params
h0_in = byref(c_double(h0))
obh2_in = byref(c_double(obh2))
och2_in = byref(c_double(och2))
mnu_in = byref(c_double(mnu))
mass_bias_in = byref(c_double(mass_bias))
Mcut_in = byref(c_double(Mcut))
M1_in = byref(c_double(M1))
kappa_in = byref(c_double(kappa))
sigma_Ncen_in = byref(c_double(sigma_Ncen))
alp_Nsat_in = byref(c_double(alp_Nsat))
rmax_in = byref(c_double(rmax))
rgs_in = byref(c_double(rgs))
flag_nu_in = byref(c_int64(flag_nu))
flag_tll_in = byref(c_int64(flag_tll))
# dNdz
nz_dndz = byref(c_int64(len(dndz)))
# integration setting
z1_in = byref(c_double(self.zmin))
z2_in = byref(c_double(self.zmax))
# outputs
nl = len(ell_arr)
cl_gg = np.zeros((2,nl))
cl_gy = np.zeros((2,nl))
tll = np.zeros((nl*2,nl*2))
ng = c_double(0.0)
nl = c_int64(nl)
self.fort_lib_cl.calc_cl_(
h0_in, obh2_in, och2_in, mnu_in,\
mass_bias_in, \
Mcut_in, M1_in, kappa_in, sigma_Ncen_in, alp_Nsat_in,\
rmax_in, rgs_in,\
byref(c_int64(self.nk_pk)), byref(c_int64(self.nz_pk)),\
np.array(kh),np.array(pk),\
np.array(dndz),nz_dndz,\
z1_in, z2_in,\
byref(c_double(z1_ng)),byref(c_double(z2_ng)),\
nl,np.array(ell_arr),\
cl_gg,cl_gy,tll,ng,\
flag_nu_in,flag_tll_in,\
c_int64(nm), c_int64(nz)
)
self.cosmo.struct_cleanup()
# k_h h/Mpc = k / Mpc
# P_h Mpc^3/h^3 = P Mpc^3
return cl_gg, cl_gy, tll, ng.value, derived, kh_arr*h0, pk_zarr, pk/h0**3
primordial_spectrum['n_s'],
'alpha_s':
primordial_spectrum['α_s'],
'k_pivot':
primordial_spectrum['pivot'] / units.Mpc**(-1),
'output':
'mPk',
'z_pk':
str(z),
'k_output_values':
'{}, {}'.format(
min(k_values) / units.Mpc**(-1),
max(k_values) / units.Mpc**(-1),
),
}
cosmo = Class()
cosmo.set(class_params_specialized)
cosmo.compute()
power_class = asarray([cosmo.pk(k / units.Mpc**(-1), z)
for k in k_values]) * units.Mpc**3
plt.loglog(k_values, power_class, 'k--', label='CLASS')
plt.xlabel(rf'$k\, [\mathrm{{{unit_length}}}^{{-1}}]$')
plt.ylabel(rf'matter power $\mathrm{{[{unit_length}^3]}}$')
plt.legend(loc='best').get_frame().set_alpha(0.7)
plt.tight_layout()
plt.savefig(fig_file)
# Compare the power spectra of the realisations with
# the power spectrum from CLASS.
# Ignore the power at the largest scales due to low
# mode count. For particles, further ignore the power at
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)
h = csm['h']
#zvec = np.array([0.0,0.5,1.0,2.0])
Pnl = e2py.get_pnonlin(csm,0.5)
kvec = Pnl['k']
kshape = kvec.shape
ClassyPars = e2py.emu_to_class(csm)
ClassyPars['Omega_Lambda']=0.0
ClassyPars['output']='mPk'
ClassyPars['non linear']='Halofit'
ClassyPars['format']='camb'
ClassyPars['P_k_max_h/Mpc']=10.
ClassyPars['k_per_decade_for_pk']=300.
ClassyPars['z_pk']=0.5#'0.0','0.5','1.0','2.0'
cosmo=Class()
cosmo.set(ClassyPars)
cosmo.compute()
pHF = np.array([cosmo.pk(k*h,0.5)*h*h*h for k in kvec]).reshape(kshape)
Fig, axs = plt.subplots(2,1, sharex=True)
ax = axs[0]
ax.loglog(kvec,Pnl['P_lin'], c='gray', label = r"$P_\rm{lin}^\rm{CLASS}$")
ax.loglog(kvec,Pnl['P_nonlin'], c='blue', label = r"$P_\rm{nl}^\rm{EE}=P_\rm{lin}^\rm{CLASS} * B$")
ax.grid(True)
ax.set_ylabel(r"P(k,z=0.5) [$(\rm{Mpc}/h)^3$]")
ax.set_xlim([0.01,5])
ax = axs[1]
ax.axhline(y=0, c="black", ls=":")
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()
# 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:]
'tau_reio': 0.06298803397344085,
'gauge': 'new',
'modes': 's',
'N_ncdm': 1,
'deg_ncdm': 3,
'T_ncdm': 0.71611,
'N_eff': 0.00641,
'Minfl': 1e+16,
'output': 'mPk tCl lCl',
'P_k_max_h/Mpc': 1
}
params = [
params1, params2, params3, params4, params5, params6, params7, params8,
params9, params10, params11, params12
]
f = open('timings.txt', 'w')
for param in params:
tic = datetime.datetime.now()
for x in range(1, 10):
cosmo = Class()
cosmo.set(param)
cosmo.compute()
cosmo.struct_cleanup()
cosmo.empty()
toc = datetime.datetime.now()
print(toc - tic)
f.write('{}\n'.format(toc - tic))
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
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 Sampler:
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 __getstate__(self):
state_dict = self.__dict__.copy()
del state_dict["mixing_matrix_evaluator"]
del state_dict["cosmo"]
del state_dict["mixing_matrix"]
del state_dict["components"]
return state_dict
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 sample_normal(self, mu, sigma, s=None):
return np.random.multivariate_normal(mu, sigma, s)
def sample_model_parameters(self):
#sampled_cosmo = self.sample_normal(self.cosmo_means, self.cosmo_var)
sampled_cosmo = np.array([
0.9665, 0.02242, 0.11933, 1.04101, 3.047, 0.0561
]) - 2 * np.array(COSMO_PARAMS_SIGMA)
#sampled_beta = self.sample_normal(self.matrix_mean, self.matrix_var).reshape((self.Npix, -1), order = "F")
sampled_beta = self.matrix_mean.reshape((self.Npix, -1), order="F")
return sampled_cosmo, sampled_beta
def sample_CMB_QU(self, cosmo_params):
params = {
'output': OUTPUT_CLASS,
'l_max_scalars': L_MAX_SCALARS,
'lensing': LENSING
}
params.update(cosmo_params)
self.cosmo.set(params)
self.cosmo.compute()
cls = self.cosmo.lensed_cl(L_MAX_SCALARS)
eb_tb = np.zeros(shape=cls["tt"].shape)
_, Q, U = hp.synfast(
(cls['tt'], cls['ee'], cls['bb'], cls['te'], eb_tb, eb_tb),
nside=self.NSIDE,
new=True)
self.cosmo.struct_cleanup()
self.cosmo.empty()
return Q, U
def sample_mixing_matrix(self, betas):
mat_pixels = []
for i in range(self.Npix):
m = self.mixing_matrix_evaluator(betas[i, :])
mat_pixels.append(m)
mixing_matrix = np.stack(mat_pixels, axis=0)
return mixing_matrix
def sample_model(self):
cosmo_params, sampled_beta = self.sample_model_parameters()
#maps = self.sample_normal(self.templates_map, self.templates_var)
cosmo_dict = {
l[0]: l[1]
for l in zip(COSMO_PARAMS_NAMES, cosmo_params.tolist())
}
tuple_QU = self.sample_CMB_QU(cosmo_dict)
map_CMB = np.stack(tuple_QU, axis=1)
'''
mixing_matrix = self.sample_mixing_matrix(sampled_beta)
map_Sync = np.stack([maps[0:self.Npix], maps[self.Npix:2*self.Npix]], axis = 1)
map_Dust = np.stack([maps[2*self.Npix:3*self.Npix], maps[3*self.Npix:]], axis = 1)
entire_map = np.stack([map_CMB, map_Dust, map_Sync], axis = 1)
dot_prod = []
for j in range(self.Npix):
m = np.dot(mixing_matrix[j, :, :], entire_map[j, :, :])
dot_prod.append(m)
sky_map = np.stack(dot_prod, axis = 0)
'''
sky_map = map_CMB
return {
"sky_map": sky_map,
"cosmo_params": cosmo_params,
"betas": sampled_beta
}
#sampler = Sampler(NSIDE)
#r = sampler.sample_model(1)
#['beta_d' 'temp' 'beta_pl']
#['beta_d' 'temp']
#['beta_pl']
class classy(BoltzmannBase):
r"""
CLASS cosmological Boltzmann code \cite{Blas:2011rf}.
"""
# Name of the Class repo/folder and version to download
_classy_repo_name = "lesgourg/class_public"
_min_classy_version = "v2.9.3"
_classy_min_gcc_version = "6.4" # Lower ones are possible atm, but leak memory!
_classy_repo_version = os.environ.get('CLASSY_REPO_VERSION',
_min_classy_version)
def initialize(self):
"""Importing CLASS from the correct path, if given, and if not, globally."""
# Allow global import if no direct path specification
allow_global = not self.path
if not self.path and self.packages_path:
self.path = self.get_path(self.packages_path)
self.classy_module = self.is_installed(path=self.path,
allow_global=allow_global)
if not self.classy_module:
raise NotInstalledError(
self.log, "Could not find CLASS. Check error message above.")
from classy import Class, CosmoSevereError, CosmoComputationError
global CosmoComputationError, CosmoSevereError
self.classy = Class()
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 = []
self.log.info("Initialized!")
def must_provide(self, **requirements):
# Computed quantities required by the likelihood
super().must_provide(**requirements)
for k, v in self._must_provide.items():
# Products and other computations
if k == "Cl":
if any(("t" in cl.lower()) for cl in v):
self.extra_args["output"] += " tCl"
if any(
(("e" in cl.lower()) or ("b" in cl.lower())) for cl in v):
self.extra_args["output"] += " pCl"
# For modern experiments, always lensed Cl's!
self.extra_args["output"] += " lCl"
self.extra_args["lensing"] = "yes"
# For l_max_scalars, remember previous entries.
self.extra_args["l_max_scalars"] = \
max(self.extra_args.get("l_max_scalars", 0), max(v.values()))
self.collectors[k] = Collector(
method="lensed_cl",
kwargs={"lmax": self.extra_args["l_max_scalars"]})
if 'T_cmb' not in self.derived_extra:
self.derived_extra += ['T_cmb']
elif k == "Hubble":
self.collectors[k] = Collector(method="Hubble",
args=[np.atleast_1d(v["z"])],
args_names=["z"],
arg_array=0)
elif k == "angular_diameter_distance":
self.collectors[k] = Collector(method="angular_distance",
args=[np.atleast_1d(v["z"])],
args_names=["z"],
arg_array=0)
elif k == "comoving_radial_distance":
self.collectors[k] = Collector(method="z_of_r",
args_names=["z"],
args=[np.atleast_1d(v["z"])])
elif isinstance(k, tuple) and k[0] == "Pk_grid":
self.extra_args["output"] += " mPk"
v = deepcopy(v)
self.add_P_k_max(v.pop("k_max"), units="1/Mpc")
# NB: Actually, only the max z is used, and the actual sampling in z
# for computing P(k,z) is controlled by `perturb_sampling_stepsize`
# (default: 0.1). But let's leave it like this in case this changes
# in the future.
self.add_z_for_matter_power(v.pop("z"))
if v["nonlinear"] and "non linear" not in self.extra_args:
self.extra_args["non linear"] = non_linear_default_code
pair = k[2:]
if pair == ("delta_tot", "delta_tot"):
v["only_clustering_species"] = False
elif pair == ("delta_nonu", "delta_nonu"):
v["only_clustering_species"] = True
else:
raise LoggedError(self.log, "NotImplemented in CLASS: %r",
pair)
self.collectors[k] = Collector(method="get_pk_and_k_and_z",
kwargs=v,
post=(lambda P, kk, z:
(kk, z, np.array(P).T)))
elif isinstance(k, tuple) and k[0] == "sigma_R":
raise LoggedError(
self.log,
"Classy sigma_R not implemented as yet - use CAMB only")
elif v is None:
k_translated = self.translate_param(k)
if k_translated not in self.derived_extra:
self.derived_extra += [k_translated]
else:
raise LoggedError(self.log, "Requested product not known: %r",
{k: v})
# Derived parameters (if some need some additional computations)
if any(("sigma8" in s) for s in self.output_params or requirements):
self.extra_args["output"] += " mPk"
self.add_P_k_max(1, units="1/Mpc")
# Adding tensor modes if requested
if self.extra_args.get("r") or "r" in self.input_params:
self.extra_args["modes"] = "s,t"
# If B spectrum with l>50, or lensing, recommend using Halofit
cls = self._must_provide.get("Cl", {})
has_BB_l_gt_50 = (any(("b" in cl.lower()) for cl in cls)
and max(cls[cl]
for cl in cls if "b" in cl.lower()) > 50)
has_lensing = any(("p" in cl.lower()) for cl in cls)
if (has_BB_l_gt_50
or has_lensing) and not self.extra_args.get("non linear"):
self.log.warning(
"Requesting BB for ell>50 or lensing Cl's: "
"using a non-linear code is recommended (and you are not "
"using any). To activate it, set "
"'non_linear: halofit|hmcode|...' in classy's 'extra_args'.")
# Cleanup of products string
self.extra_args["output"] = " ".join(
set(self.extra_args["output"].split()))
self.check_no_repeated_input_extra()
def add_z_for_matter_power(self, z):
if getattr(self, "z_for_matter_power", None) is None:
self.z_for_matter_power = np.empty(0)
self.z_for_matter_power = np.flip(np.sort(
np.unique(
np.concatenate([self.z_for_matter_power,
np.atleast_1d(z)]))),
axis=0)
self.extra_args["z_pk"] = " ".join(
["%g" % zi for zi in self.z_for_matter_power])
def add_P_k_max(self, k_max, units):
r"""
Unifies treatment of :math:`k_\mathrm{max}` for matter power spectrum:
``P_k_max_[1|h]/Mpc]``.
Make ``units="1/Mpc"|"h/Mpc"``.
"""
# Fiducial h conversion (high, though it may slow the computations)
h_fid = 1
if units == "h/Mpc":
k_max *= h_fid
# Take into account possible manual set of P_k_max_***h/Mpc*** through extra_args
k_max_old = self.extra_args.pop(
"P_k_max_1/Mpc", h_fid * self.extra_args.pop("P_k_max_h/Mpc", 0))
self.extra_args["P_k_max_1/Mpc"] = max(k_max, k_max_old)
def set(self, params_values_dict):
# If no output requested, remove arguments that produce an error
# (e.g. complaints if halofit requested but no Cl's computed.)
# Needed for facilitating post-processing
if not self.extra_args["output"]:
for k in ["non linear"]:
self.extra_args.pop(k, None)
# Prepare parameters to be passed: this-iteration + extra
args = {
self.translate_param(p): v
for p, v in params_values_dict.items()
}
args.update(self.extra_args)
# Generate and save
self.log.debug("Setting parameters: %r", args)
self.classy.set(**args)
def calculate(self, state, want_derived=True, **params_values_dict):
# Set parameters
self.set(params_values_dict)
# Compute!
try:
self.classy.compute()
# "Valid" failure of CLASS: parameters too extreme -> log and report
except CosmoComputationError as e:
if self.stop_at_error:
self.log.error(
"Computation error (see traceback below)! "
"Parameters sent to CLASS: %r and %r.\n"
"To ignore this kind of error, make 'stop_at_error: False'.",
state["params"], dict(self.extra_args))
raise
else:
self.log.debug("Computation of cosmological products failed. "
"Assigning 0 likelihood and going on. "
"The output of the CLASS error was %s" % e)
return False
# CLASS not correctly initialized, or input parameters not correct
except CosmoSevereError:
self.log.error(
"Serious error setting parameters or computing results. "
"The parameters passed were %r and %r. To see the original "
"CLASS' error traceback, make 'debug: True'.", state["params"],
self.extra_args)
raise # No LoggedError, so that CLASS traceback gets printed
# Gather products
for product, collector in self.collectors.items():
# Special case: sigma8 needs H0, which cannot be known beforehand:
if "sigma8" in self.collectors:
self.collectors["sigma8"].args[0] = 8 / self.classy.h()
method = getattr(self.classy, collector.method)
arg_array = self.collectors[product].arg_array
if arg_array is None:
state[product] = method(*self.collectors[product].args,
**self.collectors[product].kwargs)
elif isinstance(arg_array, int):
state[product] = np.zeros(
len(self.collectors[product].args[arg_array]))
for i, v in enumerate(
self.collectors[product].args[arg_array]):
args = (
list(self.collectors[product].args[:arg_array]) + [v] +
list(self.collectors[product].args[arg_array + 1:]))
state[product][i] = method(
*args, **self.collectors[product].kwargs)
elif arg_array in self.collectors[product].kwargs:
value = np.atleast_1d(
self.collectors[product].kwargs[arg_array])
state[product] = np.zeros(value.shape)
for i, v in enumerate(value):
kwargs = deepcopy(self.collectors[product].kwargs)
kwargs[arg_array] = v
state[product][i] = method(*self.collectors[product].args,
**kwargs)
if collector.post:
state[product] = collector.post(*state[product])
# Prepare derived parameters
d, d_extra = self._get_derived_all(derived_requested=want_derived)
if want_derived:
state["derived"] = {p: d.get(p) for p in self.output_params}
# Prepare necessary extra derived parameters
state["derived_extra"] = deepcopy(d_extra)
def _get_derived_all(self, derived_requested=True):
"""
Returns a dictionary of derived parameters with their values,
using the *current* state (i.e. it should only be called from
the ``compute`` method).
Parameter names are returned in CLASS nomenclature.
To get a parameter *from a likelihood* use `get_param` instead.
"""
# TODO: fails with derived_requested=False
# Put all parameters in CLASS nomenclature (self.derived_extra already is)
requested = [
self.translate_param(p)
for p in (self.output_params if derived_requested else [])
]
requested_and_extra = dict.fromkeys(
set(requested).union(set(self.derived_extra)))
# Parameters with their own getters
if "rs_drag" in requested_and_extra:
requested_and_extra["rs_drag"] = self.classy.rs_drag()
if "Omega_nu" in requested_and_extra:
requested_and_extra["Omega_nu"] = self.classy.Omega_nu
if "T_cmb" in requested_and_extra:
requested_and_extra["T_cmb"] = self.classy.T_cmb()
# Get the rest using the general derived param getter
# No need for error control: classy.get_current_derived_parameters is passed
# every derived parameter not excluded before, and cause an error, indicating
# which parameters are not recognized
requested_and_extra.update(
self.classy.get_current_derived_parameters(
[p for p, v in requested_and_extra.items() if v is None]))
# Separate the parameters before returning
# Remember: self.output_params is in sampler nomenclature,
# but self.derived_extra is in CLASS
derived = {
p: requested_and_extra[self.translate_param(p)]
for p in self.output_params
}
derived_extra = {p: requested_and_extra[p] for p in self.derived_extra}
return derived, derived_extra
def get_Cl(self, ell_factor=False, units="FIRASmuK2"):
try:
cls = deepcopy(self._current_state["Cl"])
except:
raise LoggedError(
self.log,
"No Cl's were computed. Are you sure that you have requested them?"
)
# unit conversion and ell_factor
ells_factor = ((cls["ell"] + 1) * cls["ell"] /
(2 * np.pi))[2:] if ell_factor else 1
units_factor = self._cmb_unit_factor(
units, self._current_state['derived_extra']['T_cmb'])
for cl in cls:
if cl not in ['pp', 'ell']:
cls[cl][2:] *= units_factor**2 * ells_factor
if "pp" in cls and ell_factor:
cls['pp'][2:] *= ells_factor**2 * (2 * np.pi)
return cls
def _get_z_dependent(self, quantity, z):
try:
z_name = next(k for k in ["redshifts", "z"]
if k in self.collectors[quantity].kwargs)
computed_redshifts = self.collectors[quantity].kwargs[z_name]
except StopIteration:
computed_redshifts = self.collectors[quantity].args[
self.collectors[quantity].args_names.index("z")]
i_kwarg_z = np.concatenate(
[np.where(computed_redshifts == zi)[0] for zi in np.atleast_1d(z)])
values = np.array(deepcopy(self._current_state[quantity]))
if quantity == "comoving_radial_distance":
values = values[0]
return values[i_kwarg_z]
def close(self):
self.classy.empty()
def get_can_provide_params(self):
names = [
'Omega_Lambda', 'Omega_cdm', 'Omega_b', 'Omega_m', 'rs_drag',
'z_reio', 'YHe', 'Omega_k', 'age', 'sigma8'
]
for name, mapped in self.renames.items():
if mapped in names:
names.append(name)
return names
def get_version(self):
return getattr(self.classy_module, '__version__', None)
# Installation routines
@classmethod
def get_path(cls, path):
return os.path.realpath(os.path.join(path, "code", cls.__name__))
@classmethod
def get_import_path(cls, path):
log = logging.getLogger(cls.__name__)
classy_build_path = os.path.join(path, "python", "build")
if not os.path.isdir(classy_build_path):
log.error(
"Either CLASS is not in the given folder, "
"'%s', or you have not compiled it.", path)
return None
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))
except StopIteration:
log.error(
"The CLASS installation at '%s' has not been compiled for the "
"current Python version.", path)
return None
return os.path.join(classy_build_path, post)
@classmethod
def is_compatible(cls):
import platform
if platform.system() == "Windows":
return False
return True
@classmethod
def is_installed(cls, **kwargs):
log = logging.getLogger(cls.__name__)
if not kwargs.get("code", True):
return True
path = kwargs["path"]
if path is not None and path.lower() == "global":
path = None
if path and not kwargs.get("allow_global"):
log.info("Importing *local* CLASS from '%s'.", path)
if not os.path.exists(path):
log.error("The given folder does not exist: '%s'", path)
return False
classy_build_path = cls.get_import_path(path)
if not classy_build_path:
return False
elif not path:
log.info("Importing *global* CLASS.")
classy_build_path = None
else:
log.info(
"Importing *auto-installed* CLASS (but defaulting to *global*)."
)
classy_build_path = cls.get_import_path(path)
try:
return load_module('classy',
path=classy_build_path,
min_version=cls._classy_repo_version)
except ImportError:
if path is not None and path.lower() != "global":
log.error(
"Couldn't find the CLASS python interface at '%s'. "
"Are you sure it has been installed there?", path)
else:
log.error(
"Could not import global CLASS installation. "
"Specify a Cobaya or CLASS installation path, "
"or install the CLASS Python interface globally with "
"'cd /path/to/class/python/ ; python setup.py install'")
return False
except VersionCheckError as e:
log.error(str(e))
return False
@classmethod
def install(cls,
path=None,
force=False,
code=True,
no_progress_bars=False,
**kwargs):
log = logging.getLogger(cls.__name__)
if not code:
log.info("Code not requested. Nothing to do.")
return True
log.info("Installing pre-requisites...")
exit_status = pip_install("cython")
if exit_status:
log.error("Could not install pre-requisite: cython")
return False
log.info("Downloading classy...")
success = download_github_release(os.path.join(path, "code"),
cls._classy_repo_name,
cls._classy_repo_version,
repo_rename=cls.__name__,
no_progress_bars=no_progress_bars,
logger=log)
if not success:
log.error("Could not download classy.")
return False
# Compilation
# gcc check after downloading, in case the user wants to change the compiler by
# hand in the Makefile
classy_path = cls.get_path(path)
if not check_gcc_version(cls._classy_min_gcc_version,
error_returns=False):
log.error(
"Your gcc version is too low! CLASS would probably compile, "
"but it would leak memory when running a chain. Please use a "
"gcc version newer than %s. You can still compile CLASS by hand, "
"maybe changing the compiler in the Makefile. CLASS has been "
"downloaded into %r", cls._classy_min_gcc_version, classy_path)
return False
log.info("Compiling classy...")
from subprocess import Popen, PIPE
env = deepcopy(os.environ)
env.update({"PYTHON": sys.executable})
process_make = Popen(["make"],
cwd=classy_path,
stdout=PIPE,
stderr=PIPE,
env=env)
out, err = process_make.communicate()
if process_make.returncode:
log.info(out)
log.info(err)
log.error("Compilation failed!")
return False
return True
'background_verbose': 1,
'thermodynamics_verbose': 1,
'perturbations_verbose': 1,
'primordial_verbose': 1,
'spectra_verbose': 1,
'nonlinear_verbose': 1,
'output_verbose': 1,
'transfer_verbose': 1,
'tau_reio': 0.079,
'headers': 'yes',
'modes': 's',
'k_output_values': 0.0001 + i * 0.0005,
'lensing_verbose': 1,
'radiation_streaming_trigger_tau_over_tau_k': 10000
}
cosmo = Class()
cosmo.set(params)
cosmo.compute()
# kval = 0.0001 + i/(10000*1.0)
kval = 0.0001 + i * 0.0005
data = cosmo.get_perturbations()['scalar']
print kval
print data[0]['a']
print len(data[0]['a'])
out = np.zeros(shape=(len(data[0]['a']), 4))
out[:, 0] = kval
out[:, 1] = 1.0 / data[0]['a'] - 1
from classy import Class
from scipy.optimize import fsolve
from scipy.interpolate import interp1d
from scipy.integrate import odeint
import math
font = {'size' : 16, 'family':'STIXGeneral'}
axislabelfontsize='large'
matplotlib.rc('font', **font)
matplotlib.mathtext.rcParams['legend.fontsize']='medium'
plt.rcParams["figure.figsize"] = [8.0,6.0]
M = Class()
# Table I of 1908.06995, third column, best-fit values
# Note: f and m found by trial-and-error to give the best-fit fEDE=.12, zc=10^3.562=3647.
M.set({'f_scf': 3.98e+26, 'm_scf': 5.31e-28, 'thetai_scf': 2.83, 'A_s': 2.215e-09, 'n_s': 0.9889, '100*theta_s': 1.04152, 'omega_b': 0.02253, 'omega_cdm': 0.1306, 'm_ncdm': 0.06, 'tau_reio': 0.072})
#'non linear':can choose 'halofit' or 'HMCODE'
M.set({'non linear':'HMCODE','N_ncdm':1, 'N_ur':2.0328, 'Omega_Lambda':0.0, 'Omega_fld':0, 'Omega_scf':-1, 'n_scf':3, 'CC_scf':1, 'scf_parameters':'1, 1, 1, 1, 1, 0.0', 'scf_tuning_index':3, 'attractor_ic_scf':'no', 'output':'tCl pCl lCl mPk', 'lensing':'yes', 'l_max_scalars':2508, 'P_k_max_h/Mpc':20,'z_max_pk':4.})
M.compute()
print(M.Omega_m())
baM = M.get_background()
fEDE = M.fEDE()
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")
class ModelPk(object):
""" """
logger = logging.getLogger(__name__)
def __init__(self, **param_dict):
""" """
self.params = params.copy()
self.class_params = class_params.copy()
self.set(**param_dict) #other params for CLASS
self._cosmo = None
self._redshift_func = None
self._logpk_func = None
if self.params['mpk'] is not None:
self.load_pk_from_file(self.params['mpk'])
def load_pk_from_file(self, path):
""" """
self.logger.info(f"Loading matter power spectrum from file {path}")
k, pk = np.loadtxt(path, unpack=True)
self.logger.info(f"min k: {k.min()}, max k: {k.max()}, steps {len(k)}")
pk *= self.params['bias']**2
lk = np.log(k)
lpk = np.log(pk)
self._logpk_func = interpolate.interp1d(lk,
lpk,
bounds_error=False,
fill_value=(0, 0))
def set(self, **param_dict):
""" """
for key, value in param_dict.items():
if key == 'non_linear':
key = 'non linear'
self.logger.debug("Set %s=%s", key, value)
found = False
if key in self.class_params:
self.class_params[key] = value
found = True
if key in self.params:
self.params[key] = value
found = True
if not found:
continue
@property
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 class_pk(self, k):
""" """
self.logger.info("Computing power spectrum with Class")
z = np.array([self.class_params['z_pk']]).astype('d')
shape = k.shape
k = k.flatten()
nk = len(k)
k = np.reshape(k, (nk, 1, 1))
pk = self.cosmo.get_pk(k * self.class_params['h'], z, nk, 1,
1).reshape((nk, ))
k = k.reshape((nk, ))
# set h units
pk *= self.class_params['h']**3
pk *= self.params['bias']**2
pk = pk.reshape(shape)
return pk
def get_pk(self, k):
ii = k > 0
out = np.zeros(k.shape, dtype='d')
out[ii] = np.exp(self.logpk_func(np.log(k[ii])))
return out
@property
def logpk_func(self):
if self._logpk_func is None:
k = np.logspace(self.params['log_kmin'], self.params['log_kmax'],
self.params['log_ksteps'])
pk = self.class_pk(k)
lk = np.log(k)
lpk = np.log(pk)
print("logk min", lk.min())
self._logpk_func = interpolate.interp1d(lk,
lpk,
bounds_error=False,
fill_value=(0, 0))
return self._logpk_func
def comoving_distance(self, z):
""" """
return (1 +
z) * self.cosmo.angular_distance(z) * self.class_params['h']
def redshift_at_comoving_distance(self, r):
""" """
try:
z = 10**self.redshift_func(r) - 1
except ValueError:
self.logger.error(f"r min {r.min()} max {r.max()}",
file=sys.stderr)
raise
return z
@property
def redshift_func(self):
""" """
if self._redshift_func is None:
zz = np.logspace(self.params['logzmin'], self.params['logzmax'],
self.params['logzsteps']) - 1
r = np.zeros(len(zz))
for i, z in enumerate(zz):
r[i] = self.comoving_distance(z)
self._redshift_func = interpolate.interp1d(r, np.log10(1 + zz))
return self._redshift_func
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']}
'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': '1',
'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)
#
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
class TransitionProbabilities:
def __init__(self, cosmo=None, z_reio=7.82, z_recomb=1089.80):
"""
Container class to compute dark photon transition probabilities.
:param cosmo: Cosmology as an astropy object. If `None`, defaults to Planck18 cosmology.
:param z_reio: Redshift of reionization. By default consistent with Planck18 cosmology.
:param z_recomb: Redshift of recombination. By default consistent with Planck18 cosmology.
"""
# Set constants
self.xi_3 = 1.20205 # Apéry's constant, from Wikipedia
self.eta = 6.129e-10 # Baryon-to-photon ratio, from 1912.01132
self.Y_p = 0.247 # Helium-to-hydrogen mass fraction, from 1912.01132
self.T_0 = 2.725 # CMB temperature today, from 0911.1955
self.T_0_n = (c.k_B * self.T_0 * u.Kelvin).to(u.eV).value * \
eV # CMB temperature today, in natural units
# Set cosmology
if cosmo is None:
self.cosmo = self.get_Planck18_cosmology()
else:
self.cosmo = cosmo
# Set redshift of reionization
self.z_reio = z_reio
self.z_recomb = z_recomb
# Initialize CLASS instance to compute ionization fraction from
self.initialize_class_inst()
def get_Planck18_cosmology(self):
"""
Stolen from https://github.com/abatten/fruitbat/blob/c074abff432c3b267d00fbb49781a0e0c6eeab75/fruitbat/cosmologies.py
Planck 2018 paper VI Table 2 Final column (68% confidence interval)
This is the Planck 2018 cosmology that will be added to Astropy when the
paper is accepted.
:return: astropy.cosmology.FlatLambdaCDM instance describing Planck18 cosmology
"""
planck18_cosmology = {
'Oc0':
0.2607,
'Ob0':
0.04897,
'Om0':
0.3111,
'H0':
67.66,
'n':
0.9665,
'sigma8':
0.8102,
'tau':
0.0561,
'z_reion':
7.82,
't0':
13.787,
'Tcmb0':
2.7255,
'Neff':
3.046,
'm_nu': [0., 0., 0.06],
'z_recomb':
1089.80,
'reference':
"Planck 2018 results. VI. Cosmological Parameters, "
"A&A, submitted, Table 2 (TT, TE, EE + lowE + lensing + BAO)"
}
Planck18 = FlatLambdaCDM(H0=planck18_cosmology['H0'],
Om0=planck18_cosmology['Om0'],
Tcmb0=planck18_cosmology['Tcmb0'],
Neff=planck18_cosmology['Neff'],
Ob0=planck18_cosmology['Ob0'],
name="Planck18",
m_nu=u.Quantity(planck18_cosmology['m_nu'],
u.eV))
return Planck18
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()
# z_ary = np.logspace(-3, 5, 100000)
z_ary = np.linspace(0, 33000., 300000)
x_e_ary = [self.CLASS_inst.ionization_fraction(z) for z in z_ary]
self.x_e = interp1d(z_ary, x_e_ary)
def m_A_sq(self, z, omega, x_e=None):
""" Effective photon plasma mass squared, in natural units, from 1507.02614
:param z: Redshift
:param omega: Photon frequency
:param x_e: Free electron fraction if not default (optional)
:return: Effective photon plasma mass squared, in natural units
"""
if x_e is None:
x_e = self.x_e(z)
m_A_sq = 1.4e-21 * (x_e - 6e-3 * (omega / eV)**2 *
(1 - x_e)) * (self.n_H(z) / Centimeter**-3)
return m_A_sq * eV**2 # Convert to natural units
def n_p(self, z):
""" Proton density at redshift `z` in cm^3, from 1507.02614
"""
return (1 - self.Y_p / 2.) * self.eta * 2 * self.xi_3 / np.pi**2 * (
self.T_0_n * (1 + z))**3
def n_H(self, z):
""" Number density of hydrogen nuclei at redshift `z` in cm^3
"""
return (1 -
self.Y_p) * self.eta * 2 * self.xi_3 / np.pi**2 * (self.T_0_n *
(1 + z))**3
def n_b(self, z):
""" Baryon density at redshift `z` in cm^3.
"""
return self.eta * 2 * self.xi_3 / np.pi**2 * (self.T_0_n * (1 + z))**3
def dz_dt(self, z):
""" dz/dt
"""
return -self.cosmo.H(z).value * Kmps / Mpc * (1 + z)
def omega(self, omega_0, z, evolve_z=True):
""" Frequency corresponding to present-day omega_0 evolved to `z` is `evolve_z` is `True`, otherwise
just return `omega_0`.
"""
if evolve_z:
return omega_0 * (1 + z)
else:
return omega_0
def get_z_crossings(self, m_A, omega_0, evolve_z=True):
"""
Find redshifts at which resonance occurs
:param m_A: Dark photon mass
:param omega_0: Present-day frequency
:param evolve_z: Whether to evolve frequency in redshift.
:return: Array of redshifts at which resonance occurs
"""
z_ary = np.logspace(-3, 4.5, 20000)
m_A_ary = np.nan_to_num(np.sqrt(
self.m_A_sq(z_ary, self.omega(omega_0, z_ary, evolve_z))),
nan=1e-18 * eV)
where_ary = np.where(
np.logical_or((m_A_ary[:-1] < m_A) * (m_A_ary[1:] > m_A),
(m_A_ary[:-1] > m_A) * (m_A_ary[1:] < m_A)))
def m_A_sq(z):
return np.nan_to_num(np.sqrt(
self.m_A_sq(z, self.omega(omega_0, z, evolve_z))),
nan=1e-18 * eV) - m_A
z_cross_ary = []
for i in range(len(where_ary[0])):
z_cross_ary.append(
brentq(m_A_sq, z_ary[where_ary[0][i]],
z_ary[where_ary[0][i] + 1]))
return np.array(z_cross_ary)
def P_trans(self,
m_A,
z_res_ary,
omega_0,
eps,
evolve_z=True,
approx_linearize=True):
"""
Photon transition probability
:param m_A: Dark photon mass
:param z_res_ary: Array of resonance redshifts
:param omega_0: Photon frequency (present day if `approx_linearize`, otherwise absolute)
:param eps: Kinetic mixing coupling
:param evolve_z: Whether to evolve `omega_0` in redshift
:param approx_linearize: Linearize probability in `epsilon`
:return: Transition probability array at redshifts `z_res_ary`
"""
d_log_m_A_sq_dz = np.array([
derivative(lambda z: np.log(
self.m_A_sq(z=z, omega=self.omega(omega_0, z, evolve_z))),
x0=z,
dx=1e-7) for z in z_res_ary
])
omega_res_ary = self.omega(omega_0, z_res_ary, evolve_z)
if approx_linearize:
P_homo = np.pi * m_A ** 2 * eps ** 2 / omega_res_ary * \
np.abs((d_log_m_A_sq_dz * self.dz_dt(z_res_ary))) ** -1
else:
r = np.abs((d_log_m_A_sq_dz * self.dz_dt(z_res_ary)))**-1
k = m_A**2 / (2 * omega_res_ary)
P_homo = 1 - np.exp(-2 * np.pi * r * k * np.sin(eps)**2)
return np.nan_to_num(P_homo)
def P_tot(self,
omega_0,
eps,
m_A,
approx_linearize=True,
evolve_z=True,
sum_probs=False,
**kwargs):
"""
Total conversion probability in the homogeneous limit
:param omega_0: Present-day photon frequency
:param eps: Dark photon coupling
:param m_A: Dark photon mass
:param approx_linearize: Whether to use linearized probability approximation
:param evolve_z: Whether to evolve frequency in redshift.
:param sum_probs: Whether to sum over probabilities associated with different z
:return: Redshift resonance array, transition probability array
"""
# Find redshift at which resonance occurs
z_res_ary = [
self.get_z_crossings(m_A, omega, evolve_z) for omega in omega_0
]
# Get transition probabilities at resonance
if sum_probs:
P_ary = np.array([
np.nansum(
self.P_trans(m_A,
z,
omega,
eps,
approx_linearize=approx_linearize,
evolve_z=evolve_z))
for z, omega in zip(z_res_ary, omega_0)
])
else:
P_ary = np.array([(self.P_trans(m_A,
z,
omega,
eps,
approx_linearize=approx_linearize,
evolve_z=evolve_z))
for z, omega in zip(z_res_ary, omega_0)])
return z_res_ary, 1., P_ary
def B_CMB(self, omega, T):
""" CMB spectral intensity at frequency `omega` (in natural units) for temperature `T` (in Kelvin)
"""
T_N = (c.k_B * T * u.Kelvin).to(u.eV).value * eV
return omega**3 / (2 * np.pi**2) * (np.exp(omega / T_N) - 1)**-1
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 make_LSS_data(z, cosmo_dict, outfile=None):
"""
Args:
z (float): redshift
cosmo_dict (dictionary): the CLASS cosmology
outfile (string): file to save to
"""
if outfile is None:
outfile = "LSS_dict"
LSS_dict = {}
h = cosmo_dict["h"]
Omega_b = cosmo_dict["Omega_b"]
Omega_m = cosmo_dict["Omega_cdm"] + cosmo_dict["Omega_b"]
n_s = cosmo_dict["n_s"]
cosmo = Class()
cosmo.set(cosmo_dict)
cosmo.compute()
sigma8 = cosmo.sigma8()
print("sigma8 is:", sigma8)
k = np.logspace(-5, 3, base=10, num=4000) #1/Mpc; comoving
kh = k / h #h/Mpc; comoving
r = np.logspace(-2, 3, num=1000) #Mpc/h comoving
M = np.logspace(12, 16.3, 1000) #Msun/h
z = np.asarray(z)
for zi in z:
P_nl = np.array([cosmo.pk(ki, zi) for ki in k]) * h**3
P_lin = np.array([cosmo.pk_lin(ki, zi) for ki in k]) * h**3
xi_nl = ct.xi.xi_mm_at_r(r, kh, P_nl)
xi_lin = ct.xi.xi_mm_at_r(r, kh, P_lin)
c = np.array([
conc.concentration_at_M(Mi,
kh,
P_lin,
n_s,
Omega_b,
Omega_m,
h,
Mass_type="mean") for Mi in M
])
bias = ct.bias.bias_at_M(M, kh, P_lin, Omega_m)
args_at_z = {
"k": kh,
"P_lin": P_lin,
"P_nl": P_nl,
"r": r,
"xi_lin": xi_lin,
"xi_nl": xi_nl,
"M": M,
"concentration": c,
"bias": bias,
"h": h,
"Omega_m": Omega_m,
"n_s": n_s,
"sigma8": sigma8,
"Omega_b": Omega_b
}
LSS_dict["args_at_{z:.3f}".format(z=zi)] = args_at_z
#np.save(outfile, LSS_dict)
pickle.dump(LSS_dict, open(outfile + ".p", "wb"))
print("Saved {0}".format(outfile))
return
import healpy as hp
from classy import Class
import numpy as np
cosmo = Class()
NSIDE = 512
L_MAX_SCALARS = 1500
Npix = 12 * NSIDE**2
LENSING = 'yes'
OUTPUT_CLASS = 'tCl pCl lCl'
COSMO_PARAMS_NAMES = [
"n_s", "omega_b", "omega_cdm", "100*theta_s", "ln10^{10}A_s", "tau_reio"
]
COSMO_PARAMS_MEANS = [0.9665, 0.02242, 0.11933, 1.04101, 3.047, 0.0561]
COSMO_PARAMS_SIGMA = [0.0038, 0.00014, 0.00091, 0.00029, 0.014, 0.0071]
def proposal(old_theta):
return old_theta + np.dot(
np.diag(COSMO_PARAMS_SIGMA) / 500,
np.random.normal(0, 1, size=len(COSMO_PARAMS_MEANS)))
def sample_power_spectrum(cosmo_params):
params = {
'output': OUTPUT_CLASS,
'l_max_scalars': L_MAX_SCALARS,
'lensing': LENSING
}
'adptative_stepsize': 100,
'scf_tuning_index': 0,
'do_shooting': 'yes',
'do_shooting_scf': 'yes',
# back_integration_stepsize':1'e-4
'use_big_theta_scf': 'yes',
'scf_has_perturbations': 'yes',
'attractor_ic_scf': 'no'
}
###############
#
# 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
'selection_'+ic+'_file': fname, # File for the N(z) histogram of the i-th sample }) 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)
'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 options and settings
'compute damping scale':
'yes', # needed to output the time of damping scale crossing
'gauge': 'newtonian'
}
##############
#
# call CLASS
#
M = Class()
M.set(common_settings)
M.compute()
#
# load perturbations
#
all_k = M.get_perturbations(
) # this potentially constains scalars/tensors and all k values
print all_k['scalar'][0].viewkeys()
#
one_k = all_k['scalar'][
0] # this contains only the scalar perturbations for the requested k values
#
tau = one_k['tau [Mpc]']
Theta0 = 0.25 * one_k['delta_g']
phi = one_k['phi']
class TwentyOne(LymanAlpha):
def __init__(self,
cosmo=None,
z_reio=7.82,
z_min=10.0,
z_max=150.0,
f_star_L=0.5,
f_star_X=0.004,
T_vir_cut=1e4 * Kelv,
use_hirata_fits=True,
sed_X="PL",
sed_X_kwargs={},
hmf_kwargs={
"mdef": "vir",
"model": "despali16"
}):
""" Class to calculate Lyman-alpha heating
:param cosmo: The cosmology, specified as an astropy.cosmology.FlatLambdaCDM instance
:param z_reio: Redshift at reionization, passed to CLASS
:param z_min: Minimum redshift (for interpolation tables)
:param z_max: Maximum redshift (for interpolation tables)
:param f_star_L: Efficiency of star formation, passed to Ly-A/X-ray classes
:param f_star_X: Efficiency of star formation, X-ray, passed to Ly-A/X-ray classes
:param T_vir_cut: Minimum virial temperature of star-forming halos, passed to Ly-A/X-ray classes
:param use_hirata_fits: Whether to use fitting functions from Hirata for S_alpha and T_c
:param sed_X: X-ray luminosity, "PL" or "Hirata"
:param sed_X_kwargs: Parameters for X-ray luminosity
"""
LymanAlpha.__init__(self,
cosmo=cosmo,
z_min=z_min,
z_max=z_max,
f_star_L=f_star_L,
f_star_X=f_star_X,
T_vir_cut=T_vir_cut,
sed_X=sed_X,
sed_X_kwargs=sed_X_kwargs,
hmf_kwargs=hmf_kwargs)
self.z_reio = z_reio # Redshift at reionization
self.use_hirata_fits = use_hirata_fits # Whether to use fitting functions from Hirata for S_alpha and T_c
self.data_path = str(Path(__file__).parent / "../data/")
self.load_constants() # Set class-specific constants
self.load_interpolations() # Load interpolation tables
self.initialize_class_inst() # Initialize CLASS instance
def load_constants(self):
""" Load class-specific constants
"""
self.nu_Lya = 2466 * 1e12 / Sec # Ly-A absorption frequency, originally in THz
self.gamma = 50 * 1e6 / Sec # HWHM of the 21-cm resonance, after Eq. (11) of astro-ph/0507102, originally in MHz
self.T_21 = 68.2e-3 # 21-cm temperature, in K
self.nu_21 = 1420405751.7667 / Sec # Frequency of 21-cm transition
self.A_21 = 2.86e-15 / Sec # Spontaneous emission (Einstein-A) coefficient of 21-cm transition, after Eq. (3) of 0802.2102
self.lambda_Lya = 121.567e-9 * Meter # Ly-A absorption wavelength
self.sigma_T = 0.66524587158 * barn # Thomson scattering cross-section
self.EHth = 13.6 * eV # Hydrogen ground state energy
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 x_e(self, z):
""" Electron ionization fraction, from CLASS instance
"""
return self.CLASS_inst.ionization_fraction(z)
def T_b(self, z):
""" Baryon temperature at given redshift, from CLASS instance
"""
return self.CLASS_inst.baryon_temperature(z)
def load_interpolations(self):
""" Load interpolation tables
"""
## Load table from https://github.com/ntveem/lyaheating
heffs = np.load(self.data_path + "/heffs.npy")
# heffs[:, :, :, 1, 0][np.where(heffs[:, :, :, 1, 0] < 0)] = 1e-15
# Argument arrays for T_k, T_s,...
l10_t_ary = np.linspace(np.log10(0.1), np.log10(100.0), num=175)
# ... and tau_GP (Gunn-Peterson optical depth)
l10_tau_gp_ary = np.linspace(4.0, 7.0)
# Net energy loss efficiency, defined in Eq. (37) of 1804.02406
self.E_c_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=((heffs[:, :, :, 0, 0])),
bounds_error=False,
fill_value=None)
self.E_i_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=((heffs[:, :, :, 1, 0])),
bounds_error=False,
fill_value=None)
# Energy loss to spins, defined in Eq. (32) of astro-ph/0507102
self.S_alpha_c_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=(np.log10(heffs[:, :, :, 0, 2])),
bounds_error=False,
fill_value=None)
self.S_alpha_i_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=(np.log10(heffs[:, :, :, 1, 2])),
bounds_error=False,
fill_value=None)
# Effective colour temperature, defined in Eq. (32) of astro-ph/0507102
self.T_c_c_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=(np.log10(heffs[:, :, :, 0, 3])),
bounds_error=False,
fill_value=None)
self.T_c_i_interp = RegularGridInterpolator(
points=[l10_t_ary, l10_t_ary, l10_tau_gp_ary],
values=(np.log10(heffs[:, :, :, 1, 3])),
bounds_error=False,
fill_value=None)
## Rate coefficients
# From astro-ph/0608067, Table 1
kappa_10_eH_ary = np.loadtxt(self.data_path + "/kappa_10_eH_tab.dat")
# From Zygelman (2005), http://adsabs.harvard.edu/abs/2005ApJ...622.1356Z, Table 2
kappa_10_HH_ary = np.loadtxt(self.data_path + "/kappa_10_HH_tab.dat")
self.l10_kappa_10_eH_interp = interp1d(
np.log10(kappa_10_eH_ary)[:, 0],
np.log10(kappa_10_eH_ary * Centimeter**3 / Sec)[:, 1],
bounds_error=False,
fill_value="extrapolate")
self.l10_kappa_10_HH_interp = interp1d(
np.log10(kappa_10_HH_ary)[:, 0],
np.log10(kappa_10_HH_ary * Centimeter**3 / Sec)[:, 1],
bounds_error=False,
fill_value="extrapolate")
def S_alpha_Hirata(self, Tk, Ts, tauGP):
""" Hirata fitting functional form for energy loss to spins
"""
xi = (1e-7 * tauGP)**(1.0 / 3.0) * Tk**(-2.0 / 3.0)
a = 1.0 - 0.0631789 / Tk + 0.115995 / Tk**2 - 0.401403 / Ts / Tk + 0.336463 / Ts / Tk**2
b = 1.0 + 2.98394 * xi + 1.53583 * xi**2 + 3.85289 * xi**3
return a / b
def T_c_Hirata(self, Tk, Ts):
""" Hirata fitting functional form for effective colour temperature
"""
T_c_inv = Tk**(-1.0) + 0.405535 * Tk**(-1.0) * (Ts**(-1.0) - Tk**
(-1.0))
return 1 / T_c_inv
def T_c_c(self, T_k, T_s, x_e, z):
""" Effective color temperature from interpolation, continuum
"""
if T_k <= 100.0:
return 10**self.T_c_c_interp(
[np.log10(T_k),
np.log10(T_s),
np.log10(self.tau_GP(x_e, z))])
else:
return T_k / 100 * 10**self.T_c_c_interp([
np.log10(100.0),
np.log10(T_s),
np.log10(self.tau_GP(x_e, z))
]) # self.T_b(z)
def T_c_i(self, T_k, T_s, x_e, z):
""" Effective color temperature from interpolation, injected
"""
if T_k <= 100.0:
return 10**self.T_c_i_interp(
[np.log10(T_k),
np.log10(T_s),
np.log10(self.tau_GP(x_e, z))])
else:
return T_k / 100 * 10**self.T_c_i_interp([
np.log10(100.0),
np.log10(T_s),
np.log10(self.tau_GP(x_e, z))
]) # self.T_b(z)
def x_HI(self, x_e):
""" IGM neutral fraction, from electron ionization fraction
"""
# return np.max(1 - x_e, 0)
return np.where(x_e <= 1 + self.Y_p / 4 * (1 - self.Y_p),
1 - x_e * (1 - self.Y_p / (4 - 3 * self.Y_p)), 0)
def T_CMB(self, z):
""" CMB temperature, in K
"""
return self.T_CMB_0 * (1 + z)
def tau_GP(self, x_e, z):
""" Gunn-Peterson optical depth, Eq. (35) of astro-ph/0507102
"""
H = self.cosmo.H(z).value * Kmps / Mpc
return (3 * self.n_H(z) * self.x_HI(x_e) *
self.gamma) / (2 * H * self.nu_Lya**3)
def tau_21(self, T_s, x_e, z):
""" 21-cm optical depth, Eq. (2) of 1804.02406
"""
H = self.cosmo.H(z).value * Kmps / Mpc
return 3 / (32 * np.pi) * (self.n_H(z) * self.x_HI(x_e) * self.A_21
) / (self.nu_21**3 * H) * self.T_21 / T_s
def x_CMB(self, T_s, x_e, z):
""" Spin-flip rate due to CMB heating, Eq. (14) of 1804.02406
"""
return 1 / self.tau_21(T_s, x_e,
z) * (1 - np.exp(-self.tau_21(T_s, x_e, z)))
def x_c(self, T_k, T_gamma, x_e, z):
""" Collisional coupling spin-flip rate coefficient, Eq (3) of 0802.2102
"""
return 4 * self.T_21 / (3 * self.A_21 * T_gamma) * self.n_H(z) * (
10**self.l10_kappa_10_eH_interp(np.log10(T_k)) * x_e +
10**self.l10_kappa_10_HH_interp(np.log10(T_k)))
def x_alpha_c(self, T_k, T_s, T_gamma, x_e, J_c_o_J_0, z):
""" Wouthuysen-Field spin-flip rate coefficient, continuum, Eq. (11) of astro-ph/0507102
"""
if self.use_hirata_fits:
S_alpha_c = self.S_alpha_Hirata(T_k, T_s, self.tau_GP(x_e, z))
else:
S_alpha_c = 10**self.S_alpha_c_interp(
np.log10([T_k, T_s, self.tau_GP(x_e, z)]))
return 8 * np.pi * self.lambda_Lya**2 * self.gamma * self.T_21 / (
9 * self.A_21 * T_gamma) * S_alpha_c * J_c_o_J_0 * self.J_0(z)
def x_alpha_i(self, T_k, T_s, T_gamma, x_e, J_i_o_J_0, z):
""" Wouthuysen-Field spin-flip rate coefficient, injected, Eq. (11) of astro-ph/0507102
"""
if self.use_hirata_fits:
S_alpha_i = self.S_alpha_Hirata(T_k, T_s, self.tau_GP(x_e, z))
else:
S_alpha_i = 10**self.S_alpha_i_interp(
np.log10([T_k, T_s, self.tau_GP(x_e, z)]))
return 8 * np.pi * self.lambda_Lya**2 * self.gamma * self.T_21 / (
9 * self.A_21 * T_gamma) * S_alpha_i * J_i_o_J_0 * self.J_0(z)
def T_s_inv(self, T_s, T_k, T_gamma, x_e, J, z):
""" Spin temperature (inverse), e.g. Eq (13) of 1804.02406
"""
x_CMB = self.x_CMB(T_s, x_e, z)
x_alpha_c = self.x_alpha_c(T_k, T_s, T_gamma, x_e, J[0], z)
x_alpha_i = self.x_alpha_i(T_k, T_s, T_gamma, x_e, J[1], z)
x_c = self.x_c(T_k, T_gamma, x_e, z)
if self.use_hirata_fits:
T_c_c = T_c_i = self.T_c_Hirata(T_k, T_s)
else:
T_c_c = self.T_c_c(T_k, T_s, x_e, z)
T_c_i = self.T_c_i(T_k, T_s, x_e, z)
return (x_CMB * T_gamma**-1 + x_alpha_c * T_c_c**-1 +
x_alpha_i * T_c_i**-1 + x_c * T_k**-1) / (x_CMB + x_alpha_c +
x_alpha_i + x_c)
def T_s_solve(self, T_k, T_gamma, x_e, J, z):
""" Solve for spin temperature
"""
T_s = (root(
lambda T_s: self.T_s_inv(T_s[0], T_k, T_gamma, x_e, J, z) - T_s**
-1, np.min([T_k, T_gamma])).x)[0]
return T_s
def E_CMB(self, T_s, T_gamma, T_k, x_e, z):
""" Heating efficiency due to CMB, from Eq. (17) of 1804.02406
"""
H = self.cosmo.H(z).value * Kmps / Mpc
return self.x_HI(x_e) * self.A_21 / (2 * H) * self.x_CMB(
T_s, x_e, z) * (T_gamma / T_s - 1) * self.T_21 / T_k
def E_Compton(self, T_k, x_e, z):
""" Compton heating efficiency, from Eq. (22) of 1312.4948 (TODO: but is it)
"""
H = self.cosmo.H(z).value * Kmps / Mpc
a_r = np.pi**2 / 15.0
return 8 * self.sigma_T * a_r * (self.T_CMB(z) * Kelv)**4 * x_e / (
3 * m_e * H) * (self.T_CMB(z) / T_k - 1)
def dT_k_dz(self, T_s, T_k, T_gamma, x_e, J, z):
""" Kinetic temperature evolution, from Eq (18) of 1804.02406
"""
E_c = self.E_c_interp(np.log10([T_k, T_s, self.tau_GP(x_e, z)]))
E_i = self.E_i_interp(np.log10([T_k, T_s, self.tau_GP(x_e, z)]))
dT_k_dz = 1 / (1 + z) * (
2 * T_k - 1 / (1 + self.f_He + x_e) *
(E_c * J[0] + E_i * J[1] + self.E_CMB(T_s, T_gamma, T_k, x_e, z) +
self.E_Compton(T_k, x_e, z)) * T_k)
return dT_k_dz - 1 / (1 + z) * self.heat_coeff(z)
def alpha_A(self, T):
""" Case-A recombination coefficient, from Pequignot et al (1991), Eq. (1) and Table 1
"""
zi = 1.0
a = 5.596
b = -0.6038
c = 0.3436
d = 0.4479
t = 1e-4 * T / zi**2
return zi * (a * t**b) / (1 + c * t**d) * 1e-13 * Centimeter**3 / Sec
def alpha_B(self, T):
""" Case-B recombination coefficient, from `DarkHistory`
"""
return alpha_recomb(
(k_B * T * Kelv) / eV, species="HI") * Centimeter**3 / Sec
def rec_rate(self, z, x_e, T_k, case="B"):
""" Recombination rate (Eq. 29 of 1312.4948)
"""
Gamma_rec = -peebles_C(1 - self.x_HI(x_e), 1 +
z) * self.alpha_B(T_k) * x_e**2 * self.n_H(z)
return self.dz_dt(z)**-1 * Gamma_rec
#
# deal with colors and legends
#
if i == 0:
var_color = 'k'
var_alpha = 1.
legarray.append(r'ref. $\Lambda CDM$')
else:
var_color = 'r'
var_alpha = 1. * i / (var_num - 1.)
if i == var_num - 1:
legarray.append(var_legend)
#
# call CLASS
#
M = Class()
M.set(common_settings)
M.set({var_name: var})
M.compute()
#
# get Cls
#
clM = M.lensed_cl(2500)
ll = clM['ell'][2:]
clTT = clM['tt'][2:]
clEE = clM['ee'][2:]
clPP = clM['pp'][2:]
#
# get P(k) for common k values
#
pkM = []
class classy(_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 modules
if not self.path and self.path_install:
self.path = os.path.join(
self.path_install, "code", classy_repo_rename)
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:
self.log.error("Either CLASS is not in the given folder, "
"'%s', or you have not compiled it.", self.path)
raise HandledException
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:
self.log.error(
"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'")
raise HandledException
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 = 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 current_state(self):
lasts = [self.states[i]["last"] for i in range(self.n_states)]
return self.states[lasts.index(max(lasts))]
def needs(self, **requirements):
# Computed quantities required by the likelihood
super(classy, self).needs(**requirements)
for k, v in self._needs.items():
# Products and other computations
if k == "Cl":
if any([("t" in cl.lower()) for cl in v]):
self.extra_args["output"] += " tCl"
if any([(("e" in cl.lower()) or ("b" in cl.lower())) for cl in v]):
self.extra_args["output"] += " pCl"
# For modern experiments, always lensed Cl's!
self.extra_args["output"] += " lCl"
self.extra_args["lensing"] = "yes"
# For l_max_scalars, remember previous entries.
self.extra_args["l_max_scalars"] = max(v.values())
self.collectors[k] = collector(
method="lensed_cl", kwargs={"lmax": self.extra_args["l_max_scalars"]})
elif k == "H":
self.collectors[k] = collector(
method="Hubble",
args=[np.atleast_1d(v["z"])],
args_names=["z"],
arg_array=0)
self.H_units_conv_factor = {"1/Mpc": 1, "km/s/Mpc": _c_km_s}
elif k == "angular_diameter_distance":
self.collectors[k] = collector(
method="angular_distance",
args=[np.atleast_1d(v["z"])],
args_names=["z"],
arg_array=0)
elif k == "comoving_radial_distance":
self.collectors[k] = collector(
method="z_of_r",
args_names=["z"],
args=[np.atleast_1d(v["z"])])
elif k == "Pk_interpolator":
self.extra_args["output"] += " mPk"
self.extra_args["P_k_max_h/Mpc"] = max(
v.pop("k_max"), self.extra_args.get("P_k_max_h/Mpc", 0))
self.add_z_for_matter_power(v.pop("z"))
# Use halofit by default if non-linear requested but no code specified
if v.get("nonlinear", False) and "non linear" not in self.extra_args:
self.extra_args["non linear"] = non_linear_default_code
for pair in v.pop("vars_pairs", [["delta_tot", "delta_tot"]]):
if any([x != "delta_tot" for x in pair]):
self.log.error("NotImplemented in CLASS: %r", pair)
raise HandledException
self._Pk_interpolator_kwargs = {
"logk": True, "extrap_kmax": v.pop("extrap_kmax", None)}
name = "Pk_interpolator_%s_%s" % (pair[0], pair[1])
self.collectors[name] = collector(
method="get_pk_and_k_and_z",
kwargs=v,
post=(lambda P, k, z: PowerSpectrumInterpolator(
z, k, P.T, **self._Pk_interpolator_kwargs)))
elif v is None:
k_translated = self.translate_param(k, force=True)
if k_translated not in self.derived_extra:
self.derived_extra += [k_translated]
else:
self.log.error("Requested product not known: %r", {k: v})
raise HandledException
# Derived parameters (if some need some additional computations)
if any([("sigma8" in s) for s in self.output_params or requirements]):
self.extra_args["output"] += " mPk"
self.extra_args["P_k_max_h/Mpc"] = (
max(1, self.extra_args.get("P_k_max_h/Mpc", 0)))
# Adding tensor modes if requested
if self.extra_args.get("r") or "r" in self.input_params:
self.extra_args["modes"] = "s,t"
# If B spectrum with l>50, or lensing, recommend using Halofit
cls = self._needs.get("Cl", {})
if (((any([("b" in cl.lower()) for cl in cls]) and
max([cls[cl] for cl in cls if "b" in cl.lower()]) > 50) or
any([("p" in cl.lower()) for cl in cls]) and
not self.extra_args.get("non linear"))):
self.log.warning("Requesting BB for ell>50 or lensing Cl's: "
"using a non-linear code is recommended (and you are not "
"using any). To activate it, set "
"'non_linear: halofit|hmcode|...' in classy's 'extra_args'.")
# Cleanup of products string
self.extra_args["output"] = " ".join(set(self.extra_args["output"].split()))
# If no output requested, remove arguments that produce an error
# (e.g. complaints if halofit requested but no Cl's computed.)
# Needed for facilitating post-processing
if not self.extra_args["output"]:
for k in ["non linear"]:
if k in self.extra_args:
self.log.info("Ignoring {%s: %r}, since no products requested.",
k, self.extra_args[k])
self.extra_args.pop(k)
# Finally, check that there are no repeated parameters between input and extra
if set(self.input_params).intersection(set(self.extra_args)):
self.log.error(
"The following parameters appear both as input parameters and as CLASS "
"extra arguments: %s. Please, remove one of the definitions of each.",
list(set(self.input_params).intersection(set(self.extra_args))))
raise HandledException
def add_z_for_matter_power(self, z):
if not hasattr(self, "z_for_matter_power"):
self.z_for_matter_power = np.empty((0))
self.z_for_matter_power = np.flip(np.sort(np.unique(np.concatenate(
[self.z_for_matter_power, np.atleast_1d(z)]))), axis=0)
self.extra_args["z_pk"] = " ".join(["%g" % zi for zi in self.z_for_matter_power])
def translate_param(self, p, force=False):
# "force=True" is used when communicating with likelihoods, which speak "planck"
if self.use_planck_names or force:
return self.planck_to_classy.get(p, p)
return p
def set(self, params_values_dict, i_state):
# Store them, to use them later to identify the state
self.states[i_state]["params"] = deepcopy(params_values_dict)
# Prepare parameters to be passed: this-iteration + extra
args = {self.translate_param(p): v for p, v in params_values_dict.items()}
args.update(self.extra_args)
# Generate and save
self.log.debug("Setting parameters: %r", args)
self.classy.struct_cleanup()
self.classy.set(**args)
def compute(self, _derived=None, cached=True, **params_values_dict):
lasts = [self.states[i]["last"] for i in range(self.n_states)]
try:
if not cached:
raise StopIteration
# are the parameter values there already?
i_state = next(i for i in range(self.n_states)
if self.states[i]["params"] == params_values_dict)
# has any new product been requested?
for product in self.collectors:
next(k for k in self.states[i_state] if k == product)
reused_state = True
# Get (pre-computed) derived parameters
if _derived == {}:
_derived.update(self.states[i_state]["derived"])
self.log.debug("Re-using computed results (state %d)", i_state)
except StopIteration:
reused_state = False
# update the (first) oldest one and compute
i_state = lasts.index(min(lasts))
self.log.debug("Computing (state %d)", i_state)
if self.timing:
a = time()
# Set parameters
self.set(params_values_dict, i_state)
# Compute!
try:
self.classy.compute()
# "Valid" failure of CLASS: parameters too extreme -> log and report
except CosmoComputationError:
self.log.debug("Computation of cosmological products failed. "
"Assigning 0 likelihood and going on.")
return 0
# CLASS not correctly initialized, or input parameters not correct
except CosmoSevereError:
self.log.error("Serious error setting parameters or computing results. "
"The parameters passed were %r and %r. "
"See original CLASS's error traceback below.\n",
self.states[i_state]["params"], self.extra_args)
raise # No HandledException, so that CLASS traceback gets printed
# Gather products
for product, collector in self.collectors.items():
# Special case: sigma8 needs H0, which cannot be known beforehand:
if "sigma8" in self.collectors:
self.collectors["sigma8"].args[0] = 8 / self.classy.h()
method = getattr(self.classy, collector.method)
arg_array = self.collectors[product].arg_array
if arg_array is None:
self.states[i_state][product] = method(
*self.collectors[product].args, **self.collectors[product].kwargs)
elif isinstance(arg_array, Number):
self.states[i_state][product] = np.zeros(
len(self.collectors[product].args[arg_array]))
for i, v in enumerate(self.collectors[product].args[arg_array]):
args = (list(self.collectors[product].args[:arg_array]) + [v] +
list(self.collectors[product].args[arg_array + 1:]))
self.states[i_state][product][i] = method(
*args, **self.collectors[product].kwargs)
elif arg_array in self.collectors[product].kwargs:
value = np.atleast_1d(self.collectors[product].kwargs[arg_array])
self.states[i_state][product] = np.zeros(value.shape)
for i, v in enumerate(value):
kwargs = deepcopy(self.collectors[product].kwargs)
kwargs[arg_array] = v
self.states[i_state][product][i] = method(
*self.collectors[product].args, **kwargs)
if collector.post:
self.states[i_state][product] = collector.post(
*self.states[i_state][product])
# Prepare derived parameters
d, d_extra = self._get_derived_all(derived_requested=(_derived == {}))
if _derived == {}:
_derived.update(d)
self.states[i_state]["derived"] = odict(
[[p, (_derived or {}).get(p)] for p in self.output_params])
# Prepare necessary extra derived parameters
self.states[i_state]["derived_extra"] = deepcopy(d_extra)
if self.timing:
self.n += 1
self.time_avg = (time() - a + self.time_avg * (self.n - 1)) / self.n
self.log.debug("Average running time: %g seconds", self.time_avg)
# make this one the current one by decreasing the antiquity of the rest
for i in range(self.n_states):
self.states[i]["last"] -= max(lasts)
self.states[i_state]["last"] = 1
return 1 if reused_state else 2
def _get_derived_all(self, derived_requested=True):
"""
Returns a dictionary of derived parameters with their values,
using the *current* state (i.e. it should only be called from
the ``compute`` method).
Parameter names are returned in CLASS nomenclature.
To get a parameter *from a likelihood* use `get_param` instead.
"""
# Put all pamaremters in CLASS nomenclature (self.derived_extra already is)
requested = [self.translate_param(p) for p in (
self.output_params if derived_requested else [])]
requested_and_extra = {
p: None for p in set(requested).union(set(self.derived_extra))}
# Parameters with their own getters
if "rs_drag" in requested_and_extra:
requested_and_extra["rs_drag"] = self.classy.rs_drag()
elif "Omega_nu" in requested_and_extra:
requested_and_extra["Omega_nu"] = self.classy.Omega_nu
# Get the rest using the general derived param getter
# No need for error control: classy.get_current_derived_parameters is passed
# every derived parameter not excluded before, and cause an error, indicating
# which parameters are not recognized
requested_and_extra.update(
self.classy.get_current_derived_parameters(
[p for p, v in requested_and_extra.items() if v is None]))
# Separate the parameters before returning
# Remember: self.output_params is in sampler nomenclature,
# but self.derived_extra is in CLASS
derived = {
p: requested_and_extra[self.translate_param(p)] for p in self.output_params}
derived_extra = {p: requested_and_extra[p] for p in self.derived_extra}
return derived, derived_extra
def get_param(self, p):
current_state = self.current_state()
for pool in ["params", "derived", "derived_extra"]:
value = deepcopy(
current_state[pool].get(self.translate_param(p, force=True), None))
if value is not None:
return value
self.log.error("Parameter not known: '%s'", p)
raise HandledException
def get_cl(self, ell_factor=False, units="muK2"):
current_state = self.current_state()
try:
cls = deepcopy(current_state["Cl"])
except:
self.log.error(
"No Cl's were computed. Are you sure that you have requested them?")
raise HandledException
# unit conversion and ell_factor
ell_factor = ((cls["ell"] + 1) * cls["ell"] / (2 * np.pi))[2:] if ell_factor else 1
units_factors = {"1": 1,
"muK2": _T_CMB_K * 1.e6,
"K2": _T_CMB_K}
try:
units_factor = units_factors[units]
except KeyError:
self.log.error("Units '%s' not recognized. Use one of %s.",
units, list(units_factors))
raise HandledException
for cl in cls:
if cl not in ['pp', 'ell']:
cls[cl][2:] *= units_factor ** 2 * ell_factor
if "pp" in cls and ell_factor is not 1:
cls['pp'][2:] *= ell_factor ** 2 * (2 * np.pi)
return cls
def _get_z_dependent(self, quantity, z):
try:
z_name = next(k for k in ["redshifts", "z"]
if k in self.collectors[quantity].kwargs)
computed_redshifts = self.collectors[quantity].kwargs[z_name]
except StopIteration:
computed_redshifts = self.collectors[quantity].args[
self.collectors[quantity].args_names.index("z")]
i_kwarg_z = np.concatenate(
[np.where(computed_redshifts == zi)[0] for zi in np.atleast_1d(z)])
values = np.array(deepcopy(self.current_state()[quantity]))
if quantity == "comoving_radial_distance":
values = values[0]
return values[i_kwarg_z]
def get_H(self, z, units="km/s/Mpc"):
try:
return self._get_z_dependent("H", z) * self.H_units_conv_factor[units]
except KeyError:
self.log.error("Units not known for H: '%s'. Try instead one of %r.",
units, list(self.H_units_conv_factor))
raise HandledException
def get_angular_diameter_distance(self, z):
return self._get_z_dependent("angular_diameter_distance", z)
def get_comoving_radial_distance(self, z):
return self._get_z_dependent("comoving_radial_distance", z)
def get_Pk_interpolator(self):
current_state = self.current_state()
prefix = "Pk_interpolator_"
return {k[len(prefix):]: deepcopy(v)
for k, v in current_state.items() if k.startswith(prefix)}
def close(self):
self.classy.struct_cleanup()
z_ary = np.loadtxt(
'/Users/andreacaputo/Desktop/Phd/lim-master 4/zary_forbiasav.dat')
bias_average_ary = np.loadtxt(
'/Users/andreacaputo/Desktop/Phd/lim-master 4/biasav_ary.dat')
# let's interpolate
toint = interp1d(z_ary, bias_average_ary)
def biasav(z):
return 1. * toint(z)
# create instance of the class "Class"
LambdaCDM = Class()
# pass input parameters
m1 = 0.06 / 3
m2 = 0 #0.06/3
m3 = 0 #0.06/3
LambdaCDM.set({'N_ncdm': 3})
LambdaCDM.set({'m_ncdm': str(m1) + ',' + str(m2) + ',' + str(m3)})
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
})
# The next line should be uncommented fgor higher precision (but significantly slower running)
'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()
def lookup_Pk(cosmology='planck',nonlinear=0):
"""
it saves the lookup table of the (non) linear power spectrum generate from CLASS.
If nonlinear is False (default) it generates the linear power spectrum.
You can choose between
- planck
- wmap
- ML
Choose also whether you want a nonlinear power spectrum, default is linear (nonlinear=0)
"""
# k in h/Mpc
k = N.logspace(-4., 3., 3*1024)
if nonlinear==1:
hf = 'halofit'
saveto = 'data_itam/'+cosmology+'_pk.txt'
else:
hf = ''
saveto = 'data_itam/'+cosmology+'_pk_linear.txt'
if cosmology == 'planck':
class_params = {
'non linear': hf,
'output': ['mPk','vTk'],
'P_k_max_1/Mpc': 1000.,
'z_pk': 0.,
'A_s': 2.3e-9,
'n_s': 0.96,
'h': 0.7,
'omega_b': 0.0225,
'Omega_cdm': 0.25,
}
sig8_0 = 0.8
elif cosmology == 'wmap':
class_params = {
'non linear': hf,
'output': ['mPk','vTk'],
'P_k_max_1/Mpc': 1000.,
'z_pk': 0.,
'A_s': 2.3e-9,
'n_s': 0.967,
'h': 0.704,
'omega_b': 0.02253,
'Omega_cdm': 0.226,
}
sig8_0 = 0.81
elif cosmology == 'ML':
class_params = {
'non linear': hf,
'output': ['mPk','vTk'],
'P_k_max_1/Mpc': 1000.,
'z_pk': 0.,
'A_s': 2.3e-9,
'n_s': 1.,
'h': 0.73,
'omega_b': 0.045*0.73**2,
'Omega_cdm': 0.25-0.045,
}
sig8_0 = 0.9
else:
raise ValueError("the cosmology you chose does not exist")
cosmoClass_nl = Class()
cosmoClass_nl.set(class_params)
cosmoClass_nl.compute()
# rescale the normalization of matter power spectrum to have sig8=0.8 today
sig8 = cosmoClass_nl.sigma8()
A_s = cosmoClass_nl.pars['A_s']
cosmoClass_nl.struct_cleanup() # does not clean the input class_params, cosmo.empty() does that
cosmoClass_nl.set(A_s=A_s*(sig8_0*1./sig8)**2)
cosmoClass_nl.compute()
h = cosmoClass_nl.pars['h']
pk_nl = N.asarray([ cosmoClass_nl.pk(x*h, 0.,)*h**3 for x in k ])
kpk = N.vstack((k,pk_nl))
N.savetxt(saveto,kpk)
print('saving', saveto )
return
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)
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']}
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
# The next line should be uncommented fgor higher precision (but significantly slower running)
'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()
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()
'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,
'P_k_max_1/Mpc':10.0,
'gauge':'newtonian'}
###############
#
# call CLASS a first time just to compute z_rec (will compute transfer functions at default: z=0)
#
M = Class()
M.set(common_settings)
M.compute()
derived = M.get_current_derived_parameters(['z_rec','tau_rec','conformal_age'])
#print derived.viewkeys()
z_rec = derived['z_rec']
z_rec = int(1000.*z_rec)/1000. # round down at 4 digits after coma
M.struct_cleanup() # clean output
M.empty() # clean input
#
# call CLASS again (will compute transfer functions at inout value z_rec)
#
M = Class()
M.set(common_settings)
M.set({'z_pk':z_rec})
M.compute()
# LambdaCDM parameters
'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)
class_parameters = {
'output': 'mTk,mPk',
'H0': 67.66,
'Omega_b': 0.04897,
'N_ur': 3.046,
'Omega_cdm': 0.2607,
'YHe': 0.245,
'z_reio': 7.82,
'n_s': 0.9665,
'A_s': 2.105e-9,
'P_k_max_1/Mpc': 5000.0,
'perturbed recombination': 'n',
'non linear': 'halofit'
}
M = Class()
M.set(class_parameters)
M.set({'z_pk': z_compute})
M.compute()
h = M.h() # get reduced Hubble for conversions to 1/Mpc
one_time = M.get_transfer(z_compute)
# Transfer functions
# Convert to units of Mpc^{-1}
k_ary = one_time['k (h/Mpc)'] * h
delta_b_ary = one_time['d_b']
# delta_chi_ary = one_time['d_chi']
import numpy as np
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
from classy import Class
import healpy as hp
import numpy as np
params = [('output', 'tCl pCl lCl'), ('l_max_scalars', 5000),
('lensing', 'yes'), ('n_s', 0.9665), ('omega_b', 0.02242),
('omega_cdm', 0.11933), ('100*theta_s', 1.04101),
('ln10^{10}A_s', 3.047), ('tau_reio', 0.0561)]
cosmo = Class()
cosmo.set(params)
cosmo.compute()
cls = cosmo.lensed_cl(5000)
cls_list = [it for _, it in cls.items()]
eb_tb = np.zeros(shape=cls["tt"].shape)
I, Q, U = hp.synfast(
(cls['tt'], cls['ee'], cls['bb'], cls['te'], eb_tb, eb_tb),
nside=32,
new=True)
print(Q.shape)
cosmo.struct_cleanup()
cosmo.empty()
'recfast_z_initial':z_max_pk,
#'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
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)
lnAs_fid = 3.094
As_fid = np.exp(lnAs_fid)*1e-10
ns_fid = 0.9645
mnu_fid = 0.06
h0_fid = 0.6703234
om0_fid = (obh2_fid+och2_fid+mnu_fid/93.14)/h0_fid**2
ode0_fid = 1.0-om0_fid
b2_fid = 0.0
sigma_fid = 1.0
w_fid = -1.0
# set prameters for model calculation
zmax = 5.0
kmin = 1e-2
kmax = 0.5
cosmo_fid = Class()
pars_fid = {'100*theta_s':theta_fid,'omega_b':obh2_fid,'omega_cdm':och2_fid,
'A_s':As_fid,'n_s':ns_fid,'m_ncdm':mnu_fid/3.,
'P_k_max_h/Mpc':kmax,'z_max_pk':zmax,
'output':'mPk','N_ur':0.00641,'N_ncdm':1,
'T_ncdm':0.71611,'deg_ncdm':3.}
cosmo_fid.set(pars_fid)
cosmo_fid.compute()
kh_arr = np.logspace(np.log10(kmin),np.log10(kmax),1000)
nk = 19
del_k = 0.01
kh_out = np.array([0.02+i*del_k for i in range(0,nk)])
kh_out_02 = np.tile(kh_out,2)
'''
# 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
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)
import numpy as np
from classy import Class
params = {'output': 'tCl pCl lCl', 'lensing': 'yes'}
cosmo = Class()
cosmo.set(params)
cosmo.compute()
cls = cosmo.lensed_cl(768)
for k in ['tt', 'te', 'ee', 'bb']:
cls[k] *= 1e12
np.savetxt('data/example_cls.txt',
np.array([cls['ell'], cls['tt'], cls['te'], cls['ee'],
cls['bb']]).T,
fmt=('%i %e %e %e %e'),
header='ell TT TE EE BB',
delimiter=',')
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']}
'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 options and settings
'compute damping scale':'yes', # needed to output the time of damping scale crossing
'gauge':'newtonian'}
##############
#
# call CLASS
#
M = Class()
M.set(common_settings)
M.compute()
#
# load perturbations
#
all_k = M.get_perturbations() # this potentially constains scalars/tensors and all k values
print all_k['scalar'][0].viewkeys()
#
one_k = all_k['scalar'][0] # this contains only the scalar perturbations for the requested k values
#
tau = one_k['tau [Mpc]']
Theta0 = 0.25*one_k['delta_g']
phi = one_k['phi']
psi = one_k['psi']
theta_b = one_k['theta_b']