def compute_power_spectra(self, z):
try:
from classy import Class
except ImportError:
print("Cannot precompute power spectra because CLASS "+\
"is not installed.")
return
cos = self.args['cosmology']
h = cos['h']
params = {
'output': 'mPk',
"h": h,
"sigma8": cos['sigma8'],
"n_s": cos['ns'],
"Omega_b": cos['Omega_b'],
"Omega_cdm": cos['Omega_m'] - cos['Omega_b'],
'P_k_max_1/Mpc': 1000.,
'z_max_pk': 1.0,
'non linear': 'halofit'
}
class_cosmo = Class()
class_cosmo.set(params)
class_cosmo.compute()
k = np.logspace(-5, 3, num=4000) #1/Mpc comoving
kh = k / h #h/Mpc comoving
#P(k) are in Mpc^3/h^3 comoving
Pnl = np.array([class_cosmo.pk(ki, z) for ki in k]) * h**3
Plin = np.array([class_cosmo.pk_lin(ki, z) for ki in k]) * h**3
self.args['k'] = kh
self.args['Plin'] = Plin
self.args['Pnl'] = Pnl
return
def 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 class_pk(self, z, cosmo_params=None, pk_params=None, return_s8=False):
if cosmo_params is None:
cosmo_params = self.cosmo_params
if pk_params is None:
pk_params = self.pk_params
cosmoC = Class()
h = cosmo_params['h']
class_params = {
'h': h,
'omega_b': cosmo_params['Omb'] * h**2,
'omega_cdm': (cosmo_params['Om'] - cosmo_params['Omb']) * h**2,
'A_s': cosmo_params['Ase9'] * 1.e-9,
'n_s': cosmo_params['ns'],
'output': 'mPk',
'z_max_pk': 100, #max(z)*2, #to avoid class error.
#Allegedly a compiler error, whatever that means
'P_k_max_1/Mpc': pk_params['kmax'] * h * 1.1,
}
if pk_params['non_linear'] == 1:
class_params['non linear'] = 'halofit'
class_params['N_ur'] = 3.04 #ultra relativistic species... neutrinos
if cosmo_params['mnu'] != 0:
class_params['N_ur'] -= 1 #one massive neutrino
class_params['m_ncdm'] = cosmo_params['mnu']
class_params['N_ncdm'] = 3.04 - class_params['N_ur']
if cosmo_params['w'] != -1 or cosmo_params['wa'] != 0:
class_params['Omega_fld'] = cosmo_params['Oml']
class_params['w0_fld'] = cosmo_params['w']
class_params['wa_fld'] = cosmo_params['wa']
for ke in class_accuracy_settings.keys():
class_params[ke] = class_accuracy_settings[ke]
cosmoC = Class()
cosmoC.set(class_params)
try:
cosmoC.compute()
except Exception as err:
print(class_params, err)
raise Exception('Class crashed')
k = self.kh * h
pkC = np.array([[cosmoC.pk(ki, zj) for ki in k] for zj in z])
pkC *= h**3
s8 = cosmoC.sigma8()
cosmoC.struct_cleanup()
if return_s8:
return pkC, self.kh, s8
else:
return pkC, self.kh
def class_pk(self, z, cosmo_params=None, pk_params=None, return_s8=False):
#output is in same unit as CAMB
if not cosmo_params:
cosmo_params = self.cosmo_params
if not pk_params:
pk_params = self.pk_params
kh = np.logspace(np.log10(pk_params['kmin']),
np.log10(pk_params['kmax']), pk_params['nk'])
cosmoC = Class()
h = cosmo_params['h']
class_params = {
'h': h,
'omega_b': cosmo_params['Omb'] * h**2,
'omega_cdm': (cosmo_params['Om'] - cosmo_params['Omb']) * h**2,
'A_s': cosmo_params['As'],
'n_s': cosmo_params['ns'],
'output': 'mPk',
'z_max_pk': max(z) + 0.1,
'P_k_max_1/Mpc': pk_params['kmax'] * h,
}
if pk_params['non_linear'] == 1:
class_params['non linear'] = 'halofit'
class_params['N_ur'] = 3.04 #ultra relativistic species... neutrinos
if cosmo_params['mnu'] != 0:
class_params['N_ur'] -= 1 #one massive neutrino
class_params['m_ncdm'] = cosmo_params['mnu']
class_params['N_ncdm'] = 3.04 - class_params['N_ur']
if cosmo_params['w'] != -1:
class_params['Omega_fld'] = cosmo_params['Oml']
class_params['w0_fld'] = cosmo_params['w']
class_params['wa_fld'] = cosmo_params['wa']
for ke in class_accuracy_settings.keys():
class_params[ke] = class_accuracy_settings[ke]
cosmoC = Class()
cosmoC.set(class_params)
cosmoC.compute()
k = kh * h #output is in same unit as CAMB
pkC = np.array([[cosmoC.pk(ki, zj) for ki in k] for zj in z])
pkC *= h**3
s8 = cosmoC.sigma8()
cosmoC.struct_cleanup()
if return_s8:
return pkC, kh, s8
else:
return pkC, kh
def calculate_power(cosmology,
k_min,
k_max,
z=0,
num_k=500,
scaled_by_h=True,
n_s=0.9619,
logA=3.0980):
"""
Calculate the power spectrum P(k,z) over the range k_min <= k <= k_max.
"""
try:
from classy import Class
cosmo = Class()
except ImportError:
raise RuntimeError('power.calculate_power requires classy.')
class_parameters = get_class_parameters(cosmology)
class_parameters['output'] = 'mPk'
if scaled_by_h:
class_parameters['P_k_max_h/Mpc'] = k_max
else:
class_parameters['P_k_max_1/Mpc'] = k_max
class_parameters['n_s'] = n_s
class_parameters['ln10^{10}A_s'] = logA
cosmo.set(class_parameters)
cosmo.compute()
if scaled_by_h:
k_scale = cosmo.h()
Pk_scale = cosmo.h()**3
else:
k_scale = 1.
Pk_scale = 1.
result = np.empty((num_k, ), dtype=[('k', float), ('Pk', float)])
result['k'][:] = np.logspace(np.log10(k_min), np.log10(k_max), num_k)
for i, k in enumerate(result['k']):
result['Pk'][i] = cosmo.pk(k * k_scale, z) * Pk_scale
cosmo.struct_cleanup()
cosmo.empty()
return result
def get_theory_pk(k, params):
"""Returns theoretical p(k) at z=0 given k values with given cosmo params
Parameters
----------
k : array-like
k values at which to evaluate theoretical p(k)
params : dict
Cosmological parameters to pass to Class
Returns
-------
Pk*norm : array
Theoretical p(k), with proper normalization
"""
# initialize class
cosmo = Class()
# set the class parameters
# cosmo.set(
# {
# "output": "mPk",
# "P_k_max_1/Mpc": 10.0,
# "omega_b": params["omega_b"],
# "omega_cdm": params["omega_cdm"],
# "h": params["h"],
# "A_s": params["A_s"],
# "n_s": params["n_s"],
# "tau_reio": params["tau_reio"],
# }
# )
cosmo.set({"output": "mPk", "P_k_max_1/Mpc": 10.0, **params})
# run class
cosmo.compute()
# calculate Pk at all k values (note the normalization for k and P_k)
h = params["h"]
return [cosmo.pk(kk * h, 0) * h**3 for kk in k]
def calculate_power(cosmology, k_min, k_max, z=0, num_k=500, scaled_by_h=True,
n_s=0.9619, logA=3.0980):
"""
Calculate the power spectrum P(k,z) over the range k_min <= k <= k_max.
"""
try:
from classy import Class
cosmo = Class()
except ImportError:
raise RuntimeError('power.calculate_power requires classy.')
class_parameters = get_class_parameters(cosmology)
class_parameters['output'] = 'mPk'
if scaled_by_h:
class_parameters['P_k_max_h/Mpc'] = k_max
else:
class_parameters['P_k_max_1/Mpc'] = k_max
class_parameters['n_s'] = n_s
class_parameters['ln10^{10}A_s'] = logA
cosmo.set(class_parameters)
cosmo.compute()
if scaled_by_h:
k_scale = cosmo.h()
Pk_scale = cosmo.h()**3
else:
k_scale = 1.
Pk_scale = 1.
result = np.empty((num_k,), dtype=[('k', float), ('Pk', float)])
result['k'][:] = np.logspace(np.log10(k_min), np.log10(k_max), num_k)
for i, k in enumerate(result['k']):
result['Pk'][i] = cosmo.pk(k * k_scale, z) * Pk_scale
cosmo.struct_cleanup()
cosmo.empty()
return result
def calc_power_spec(cosmos, zs, lin=True):
Nc = len(cosmos)
Nz = len(zs)
ps = np.zeros((Nc, Nz, len(k)))
for i in range(Nc):
obh2, och2, w, ns, ln10As, H0, Neff, s8 = cosmos[i]
h = H0 / 100.
Omega_b = obh2 / h**2
Omega_c = och2 / h**2
Omega_m = Omega_b + Omega_c
params = {
'output': 'mPk',
'h': h,
'ln10^{10}A_s': ln10As,
'n_s': ns,
'w0_fld': w,
'wa_fld': 0.0,
'Omega_b': Omega_b,
'Omega_cdm': Omega_c,
'Omega_Lambda': 1. - Omega_m,
'N_eff': Neff,
'P_k_max_1/Mpc': 10.,
'z_max_pk': 5.
}
if not lin:
params['non linear'] = 'halofit'
cosmo = Class()
cosmo.set(params)
cosmo.compute()
for j in range(len(zs)):
if lin:
ps[i, j] = np.array([cosmo.pk_lin(ki, zs[j]) for ki in k])
else:
ps[i, j] = np.array([cosmo.pk(ki, zs[j]) for ki in k])
continue
print("Finished box%d" % i)
return ps
cosmo.set({
'output': 'mPk',
'Omega_cdm': omM0 - omB0,
'Omega_k': 1. - omM0 - omL0,
'h': h,
'sigma8': sig8,
'n_s': ns,
'non linear': '',
'P_k_max_h/Mpc': 10.,
'z_pk': zlist,
'output_verbose': 1
})
cosmo.compute()
print "----"
klin = 10**np.linspace(-4, 0.0, 100)
pk0 = np.array([cosmo.pk(kk, 0.) for kk in klin])
pkz = np.array([cosmo.pk(kk, zred) for kk in klin])
# save P(k) lin to be used for TNS model
np.savetxt("pklin_z0_wmap9.dat", zip(klin, pk0))
print "----"
#"""
zlist_nl = str("0, %.1f" % (zred))
print zlist_nl
cosmo = Class()
print omM0 - omB0, 1. - omM0 - omL0, h, sig8, ns, zlist_nl
cosmo.set({
'output': 'mPk',
'Omega_cdm': omM0 - omB0,
DV = (z * (1 + z)**2 * da**2 / cosmo.Hubble(z))**(1. / 3.)
# print('DV=',DV)
fs8 = cosmo.scale_independent_growth_factor(
z) * cosmo.scale_independent_growth_factor_f(z) * cosmo.sigma8()
# print('fs8=',fs8)
# print('sigma8=',cosmo.sigma8())
print('rd/DV=', rd / DV)
print('rdH=', rd * cosmo.Hubble(z))
print('rd/DA=', rd / da)
P2noW = np.zeros(k_size)
P0noW = np.zeros(k_size)
for i in range(k_size):
kinloop1 = k[i] * h
P2noW[i] = (
norm**2. * cosmo.pk(kinloop1, z)[18] + norm**4. *
(cosmo.pk(kinloop1, z)[24]) +
norm**1. * b1 * cosmo.pk(kinloop1, z)[19] + norm**3. * b1 *
(cosmo.pk(kinloop1, z)[25]) + b1**2. * norm**2. *
cosmo.pk(kinloop1, z)[26] + b1 * b2 * norm**2. * cosmo.pk(
kinloop1, z)[34] + b2 * norm**3. * cosmo.pk(kinloop1, z)[35] +
b1 * bG2 * norm**2. * cosmo.pk(kinloop1, z)[36] +
bG2 * norm**3. * cosmo.pk(kinloop1, z)[37] + 2. *
(css2) * norm**2. * cosmo.pk(kinloop1, z)[12] / h**2. +
(2. * bG2 + 0.8 * bGamma3) * norm**3. * cosmo.pk(kinloop1, z)[9]
) * h**3. + fz**2. * b4 * k[i]**2. * (
(norm**2. * fz**2. * 70. + 165. * fz * b1 * norm + 99. * b1**2.) *
4. / 693.) * (35. / 8.) * cosmo.pk(kinloop1, z)[13] * h
P0noW[i] = (
norm**2. * cosmo.pk(kinloop1, z)[15] + norm**4. *
(cosmo.pk(kinloop1, z)[21]) +
# normal hierarchy
[m1, m2, m3] = get_masses(2.45e-3,7.50e-5, sum_masses, 'NH')
NH = Class()
NH.set(commonsettings)
NH.set({'m_ncdm':str(m1)+','+str(m2)+','+str(m3)})
NH.compute()
# inverted hierarchy
[m1, m2, m3] = get_masses(2.45e-3,7.50e-5, sum_masses, 'IH')
IH = Class()
IH.set(commonsettings)
IH.set({'m_ncdm':str(m1)+','+str(m2)+','+str(m3)})
IH.compute()
pkNH = []
pkIH = []
for k in kvec:
pkNH.append(NH.pk(k,0.))
pkIH.append(IH.pk(k,0.))
NH.struct_cleanup()
IH.struct_cleanup()
# extract h value to convert k from 1/Mpc to h/Mpc
h = NH.h()
plt.semilogx(kvec/h,1-np.array(pkNH)/np.array(pkIH))
legarray.append(r'$\Sigma m_i = '+str(sum_masses)+'$eV')
plt.axhline(0,color='k')
plt.xlim(kvec[0]/h,kvec[-1]/h)
plt.xlabel(r'$k [h \mathrm{Mpc}^{-1}]$')
plt.ylabel(r'$1-P(k)^\mathrm{NH}/P(k)^\mathrm{IH}$')
plt.legend(legarray)
# normal hierarchy
[m1, m2, m3] = get_masses(2.45e-3, 7.50e-5, sum_masses, 'NH')
NH = Class()
NH.set(commonsettings)
NH.set({'m_ncdm': str(m1) + ',' + str(m2) + ',' + str(m3)})
NH.compute()
# inverted hierarchy
[m1, m2, m3] = get_masses(2.45e-3, 7.50e-5, sum_masses, 'IH')
IH = Class()
IH.set(commonsettings)
IH.set({'m_ncdm': str(m1) + ',' + str(m2) + ',' + str(m3)})
IH.compute()
pkNH = []
pkIH = []
for k in kvec:
pkNH.append(NH.pk(k, 0.))
pkIH.append(IH.pk(k, 0.))
NH.struct_cleanup()
IH.struct_cleanup()
# extract h value to convert k from 1/Mpc to h/Mpc
h = NH.h()
plt.semilogx(kvec / h, 1 - np.array(pkNH) / np.array(pkIH))
legarray.append(r'$\Sigma m_i = ' + str(sum_masses) + '$eV')
plt.axhline(0, color='k')
plt.xlim(kvec[0] / h, kvec[-1] / h)
plt.xlabel(r'$k [h \mathrm{Mpc}^{-1}]$')
plt.ylabel(r'$1-P(k)^\mathrm{NH}/P(k)^\mathrm{IH}$')
plt.legend(legarray)
# In[6]:
# with all the struct_init() methods called. cosmo.compute() # Access the lensed cl cls = cosmo.lensed_cl() # Print on screen to see the output print "cls contains:", cls.viewkeys() #print cls # It is a dictionnary that contains the fields: tt, te, ee, bb, pp, tp #cosmo.get_current_derived_parameters(['Omega_Lambda']) #cosmo.pk(k, z) # function that returns P(k,z). Watch that there is no h factor anywhere K = np.logspace(np.log10(1.e-4), np.log10(3.), 501, 10.) f = lambda k: cosmo.pk(k, 0.) Plin = np.array(map(f, K)) plt.loglog(K, Plin) plt.show() # Clean CLASS (the equivalent of the struct_free() in the `main` # of CLASS. This step is primordial when running in a loop over different # cosmologies, as you will saturate your memory very fast if you ommit # it. #cosmo.struct_cleanup() # If you want to change completely the cosmology, you should also # clean the arguments, otherwise, if you are simply running on a loop # of different values for the same parameters, this step is not needed #cosmo.empty()
# one_k = all_k['scalar'][0] # this contains only the scalar perturbations for the requested k values
clM = M.lensed_cl(2500)
ll_LCDM = clM['ell'][2:]
clTT_LCDM = clM['tt'][2:]
clEE_LCDM = clM['ee'][2:]
clTE_LCDM = clM['te'][2:]
clPP_LCDM = clM['pp'][2:]
# ax_TT.semilogx(ll_LCDM,(ll_LCDM)*(ll_LCDM+1)/(2*np.pi)*(clTT_LCDM),'k',lw=5)
# ax_EE.semilogx(ll_LCDM,(ll_LCDM)*(ll_LCDM+1)/(2*np.pi)*(clEE_LCDM),'k',lw=5)
# ax_PP.semilogx(ll_LCDM,(ll_LCDM)*(ll_LCDM+1)/(2*np.pi)*(clPP_LCDM),'k',lw=5)
pkM_LCDM = []
for k in kvec:
pkM_LCDM.append(M.pk(k, 0.))
# i=i+1
fTT = interp1d(ll_LCDM, clTT_LCDM)
fEE = interp1d(ll_LCDM, clEE_LCDM)
fTE = interp1d(ll_LCDM, clTE_LCDM)
M.struct_cleanup()
for i in range(var_num):
#
# deal with varying parameters:
#
dashes = dashes_array[i]
#
l = []
print(' * Compute with %s=%d' % (var_name1, n[i]))
delta_b_ary = one_time['d_b']
delta_chi_ary = one_time['d_chi']
n_s = M.n_s()
# Primordial PS
k_pivot = 0.05
P_s = 2.105e-9 * (k_ary / k_pivot) ** (n_s - 1)
# Power spectra from transfer function
# In units of Mpc^3 / h^3
Pk_b_ary = P_s * (delta_b_ary) ** 2 / (k_ary ** 3 / (2 * np.pi ** 2)) * h ** 3
Pk_chi_ary = P_s * (delta_chi_ary) ** 2 / \
(k_ary ** 3 / (2 * np.pi ** 2)) * h ** 3
Pk_chi_b_ary = P_s * (delta_chi_ary * delta_b_ary) / \
(k_ary ** 3 / (2 * np.pi ** 2)) * h ** 3
Pk_tot_lin_ary = np.array([M.pk_lin(k, z_compute) * h ** 3 for k in k_ary])
Pk_tot_nonlin_ary = np.array([M.pk(k, z_compute) * h ** 3 for k in k_ary])
np.savez(output_dir + "p_k_chi_k_max_500_z_" + str(iz_compute),
k_ary=k_ary / h, # Convert back to h / Mpc
Pk_b_ary=Pk_b_ary,
Pk_chi_ary=Pk_chi_ary,
Pk_chi_b_ary=Pk_chi_b_ary,
Pk_e_ary=Pk_b_ary + Pk_chi_ary + 2 * Pk_chi_b_ary,
Pk_e_b_ary=Pk_chi_b_ary + Pk_b_ary,
Pk_tot_lin_ary=Pk_tot_lin_ary,
Pk_tot_nonlin_ary=Pk_tot_nonlin_ary
)
class class_interface:
def __init__(self, cosmology=None):
'''
This is an interface I made to talk to the cython
module for class.
The inputs are a dictionary containing cosmological parameters.
If the input cosmology is not given, then the default is Planck 2015.
'''
if (cosmology == None):
# default to planck cosmology 2015
params = {
'output': 'tCl lCl,mPk, mTk',
'l_max_scalars': 2000,
'A_s': 2.3e-9,
'n_s': 0.9624,
'h': 0.6774,
'omega_b': 0.02230,
'omega_cdm': 0.1188,
'k_pivot': 0.05,
'A_s': 2.142e-9,
'n_s': 0.9667,
'P_k_max_1/Mpc': 500.0,
'N_eff': 3.04,
'Omega_fld': 0,
'YHe': 0.2453,
'z_reio': 8.8
}
else:
params = cosmology
self.k_max = params['P_k_max_1/Mpc']
self.cosmo = Class()
self.cosmo.set(params)
self.cosmo.compute()
params = {
'output': 'tCl lCl,mPk, mTk',
'l_max_scalars': 2000,
'z_pk': 0,
'A_s': 2.3e-9,
'n_s': 0.9624,
'h': 0.6774,
'omega_b': 0.02230,
'omega_cdm': 0.1188,
'k_pivot': 0.05,
'A_s': 2.142e-9,
'n_s': 0.9667,
'P_k_max_1/Mpc': 500.0,
'N_eff': 3.04,
'Omega_fld': 0,
'YHe': 0.2453,
'z_reio': 8.8,
'non linear': 'halofit'
}
self.non_linear_cosmo = Class()
self.non_linear_cosmo.set(params)
self.non_linear_cosmo.compute()
def nl_power(self, k, z):
P = np.zeros(k.size)
for i in range(k.size):
P[i] = self.non_linear_cosmo.pk(k[i], z)
return P
def linear_power(self, k):
if k[-1] > self.k_max:
print 'power spectrum requested is out of bounds'
print 'adjust parameters sent to class'
sys.exit()
P = np.zeros(k.size)
for i in range(k.size):
P[i] = self.cosmo.pk(k[i], 0)
return P
'Ob0': Omega_b,
'sigma8': sigma8,
'ns': n_s
}
cosmology.addCosmology('fox', foxcolossus)
cosmology.setCosmology('fox')
cosmo = cosmology.setCosmology('fox')
Om = Omega_m
# Power spectrum
k = np.logspace(-4, 2, num=1000) # 1/Mpc
z = 0.
Plin = np.array([classcosmo.pk_lin(ki, z) for ki in k])
Pnl = np.array([classcosmo.pk(ki, z) for ki in k])
# NOTE: You will need to convert these to h/Mpc and (Mpc/h)^3
# to use in the toolkit. To do this you would do:
k /= foxclass['h']
Plin *= foxclass['h']**3
Pnl *= foxclass['h']**3
# End of set cosmology
# Read xihm data
################# Mbin 2e14-5e14 Msun/h #################
xihm = np.loadtxt('data/xihm1_2e14_5e14_z0.0_down10')
covxihm = np.loadtxt('data/covxihm1_2e14_5e14_z0.0_down10')
r = np.loadtxt('data/r')
# Parameters
rt = 1.832
def Sijkl(z_arr,
windows,
cosmo_params=default_cosmo_params,
precision=10,
tol=1e-3,
cosmo_Class=None):
# Assert everything as the good type and shape, and find number of redshifts, bins etc
zz = np.asarray(z_arr)
win = np.asarray(windows)
assert zz.ndim == 1, 'z_arr must be a 1-dimensional array'
assert win.ndim == 2, 'windows must be a 2-dimensional array'
nz = len(zz)
nbins = win.shape[0]
assert win.shape[1] == nz, 'windows must have shape (nbins,nz)'
assert zz.min() > 0, 'z_arr must have values > 0'
# If the cosmology is not provided (in the same form as CLASS), run CLASS
if cosmo_Class is None:
cosmo = Class()
dico_for_CLASS = cosmo_params
dico_for_CLASS['output'] = 'mPk'
cosmo.set(dico_for_CLASS)
cosmo.compute()
else:
cosmo = cosmo_Class
h = cosmo.h() #for conversions Mpc/h <-> Mpc
# Define arrays of r(z), k, P(k)...
zofr = cosmo.z_of_r(zz)
comov_dist = zofr[0] #Comoving distance r(z) in Mpc
dcomov_dist = 1 / zofr[1] #Derivative dr/dz in Mpc
dV = comov_dist**2 * dcomov_dist #Comoving volume per solid angle in Mpc^3/sr
growth = np.zeros(nz) #Growth factor
for iz in range(nz):
growth[iz] = cosmo.scale_independent_growth_factor(zz[iz])
#Index pairs of bins
npairs = (nbins * (nbins + 1)) // 2
pairs = np.zeros((2, npairs), dtype=int)
count = 0
for ibin in range(nbins):
for jbin in range(ibin, nbins):
pairs[0, count] = ibin
pairs[1, count] = jbin
count += 1
# Compute normalisations
Inorm = np.zeros(npairs)
Inorm2D = np.zeros((nbins, nbins))
for ipair in range(npairs):
ibin = pairs[0, ipair]
jbin = pairs[1, ipair]
integrand = dV * windows[ibin, :] * windows[jbin, :]
integral = integrate.simps(integrand, zz)
Inorm[ipair] = integral
Inorm2D[ibin, jbin] = integral
Inorm2D[jbin, ibin] = integral
#Flag pairs with too small overlap as unreliable
#Note: this will also speed up later computations
#Default tolerance : tol=1e-3
flag = np.zeros(npairs, dtype=int)
for ipair in range(npairs):
ibin = pairs[0, ipair]
jbin = pairs[1, ipair]
ratio = abs(Inorm2D[ibin, jbin]) / np.sqrt(
abs(Inorm2D[ibin, ibin] * Inorm2D[jbin, jbin]))
if ratio < tol:
flag[ipair] = 1
# Compute U(i,j;kk)
keq = 0.02 / h #Equality matter radiation in 1/Mpc (more or less)
klogwidth = 10 #Factor of width of the integration range. 10 seems ok
kmin = min(keq, 1. / comov_dist.max()) / klogwidth
kmax = max(keq, 1. / comov_dist.min()) * klogwidth
nk = 2**precision #10 seems to be enough. Increase to test precision, reduce to speed up.
#kk = np.linspace(kmin,kmax,num=nk) #linear grid on k
logkmin = np.log(kmin)
logkmax = np.log(kmax)
logk = np.linspace(logkmin, logkmax, num=nk)
kk = np.exp(logk) #logarithmic grid on k
Pk = np.zeros(nk)
for ik in range(nk):
Pk[ik] = cosmo.pk(kk[ik], 0.) #In Mpc^3
Uarr = np.zeros((npairs, nk))
for ipair in range(npairs):
if flag[ipair] == 0:
ibin = pairs[0, ipair]
jbin = pairs[1, ipair]
for ik in range(nk):
kr = kk[ik] * comov_dist
integrand = dV * windows[ibin, :] * windows[
jbin, :] * growth * np.sin(kr) / kr
Uarr[ipair, ik] = integrate.simps(integrand, zz)
# Compute Sijkl finally
Cl_zero = np.zeros((nbins, nbins, nbins, nbins))
#For ipair<=jpair
for ipair in range(npairs):
if flag[ipair] == 0:
U1 = Uarr[ipair, :] / Inorm[ipair]
ibin = pairs[0, ipair]
jbin = pairs[1, ipair]
for jpair in range(ipair, npairs):
if flag[jpair] == 0:
U2 = Uarr[jpair, :] / Inorm[jpair]
kbin = pairs[0, jpair]
lbin = pairs[1, jpair]
integrand = kk**2 * Pk * U1 * U2
#integral = 2/(i * integrate.simps(integrand,kk) #linear integration
integral = 2 / pi * integrate.simps(integrand * kk,
logk) #log integration
#Run through all valid symmetries to fill the 4D array
#Symmetries: i<->j, k<->l, (i,j)<->(k,l)
Cl_zero[ibin, jbin, kbin, lbin] = integral
Cl_zero[ibin, jbin, lbin, kbin] = integral
Cl_zero[jbin, ibin, kbin, lbin] = integral
Cl_zero[jbin, ibin, lbin, kbin] = integral
Cl_zero[kbin, lbin, ibin, jbin] = integral
Cl_zero[kbin, lbin, jbin, ibin] = integral
Cl_zero[lbin, kbin, ibin, jbin] = integral
Cl_zero[lbin, kbin, jbin, ibin] = integral
Sijkl = Cl_zero / (4 * pi)
return Sijkl
# End of PySSC.py
def turboSij(zstakes=default_zstakes,
cosmo_params=default_cosmo_params,
cosmo_Class=None):
# If the cosmology is not provided (in the same form as CLASS), run CLASS
if cosmo_Class is None:
cosmo = Class()
dico_for_CLASS = cosmo_params
dico_for_CLASS['output'] = 'mPk'
cosmo.set(dico_for_CLASS)
cosmo.compute()
else:
cosmo = cosmo_Class
h = cosmo.h() #for conversions Mpc/h <-> Mpc
# Define arrays of z, r(z), k, P(k)...
nzbins = len(zstakes) - 1
nz_perbin = 10
z_arr = np.zeros((nz_perbin, nzbins))
comov_dist = np.zeros((nz_perbin, nzbins))
for j in range(nzbins):
z_arr[:, j] = np.linspace(zstakes[j], zstakes[j + 1], nz_perbin)
comov_dist[:, j] = (cosmo.z_of_r(z_arr[:, j]))[0] #In Mpc
keq = 0.02 / h #Equality matter radiation in 1/Mpc (more or less)
klogwidth = 10 #Factor of width of the integration range. 10 seems ok ; going higher needs to increase nk_fft to reach convergence (fine cancellation issue noted in Lacasa & Grain)
kmin = min(keq, 1. / comov_dist.max()) / klogwidth
kmax = max(keq, 1. / comov_dist.min()) * klogwidth
nk_fft = 2**11 #seems to be enough. Increase to test precision, reduce to speed up.
k_4fft = np.linspace(kmin, kmax,
nk_fft) #linear grid on k, as we need to use an FFT
Deltak = kmax - kmin
Dk = Deltak / nk_fft
Pk_4fft = np.zeros(nk_fft)
for ik in range(nk_fft):
Pk_4fft[ik] = cosmo.pk(k_4fft[ik], 0.) #In Mpc^3
dr_fft = np.linspace(0, nk_fft // 2, nk_fft // 2 + 1) * 2 * pi / Deltak
# Compute necessary FFTs and make interpolation functions
fft2 = np.fft.rfft(Pk_4fft / k_4fft**2) * Dk
dct2 = fft2.real
dst2 = -fft2.imag
km2Pk_dct = interp1d(dr_fft, dct2, kind='cubic')
fft3 = np.fft.rfft(Pk_4fft / k_4fft**3) * Dk
dct3 = fft3.real
dst3 = -fft3.imag
km3Pk_dst = interp1d(dr_fft, dst3, kind='cubic')
fft4 = np.fft.rfft(Pk_4fft / k_4fft**4) * Dk
dct4 = fft4.real
dst4 = -fft4.imag
km4Pk_dct = interp1d(dr_fft, dct4, kind='cubic')
# Compute Sij finally
Sij = np.zeros((nzbins, nzbins))
for j1 in range(nzbins):
rmin1 = (comov_dist[:, j1]).min()
rmax1 = (comov_dist[:, j1]).max()
zmean1 = ((z_arr[:, j1]).min() + (z_arr[:, j1]).max()) / 2
growth1 = cosmo.scale_independent_growth_factor(zmean1)
pref1 = 3. * growth1 / (rmax1**3 - rmin1**3)
for j2 in range(nzbins):
rmin2 = (comov_dist[:, j2]).min()
rmax2 = (comov_dist[:, j2]).max()
zmean2 = ((z_arr[:, j2]).min() + (z_arr[:, j2]).max()) / 2
growth2 = cosmo.scale_independent_growth_factor(zmean2)
pref2 = 3. * growth2 / (rmax2**3 - rmin2**3)
#p1p2: rmax1 & rmax2
rsum = rmax1 + rmax2
rdiff = abs(rmax1 - rmax2)
rprod = rmax1 * rmax2
Icp2 = km2Pk_dct(rsum)
Icm2 = km2Pk_dct(rdiff)
Isp3 = km3Pk_dst(rsum)
Ism3 = km3Pk_dst(rdiff)
Icp4 = km4Pk_dct(rsum)
Icm4 = km4Pk_dct(rdiff)
Fp1p2 = -Icp4 + Icm4 - rsum * Isp3 + rdiff * Ism3 + rprod * (Icp2 +
Icm2)
#p1m2: rmax1 & rmin2
rsum = rmax1 + rmin2
rdiff = abs(rmax1 - rmin2)
rprod = rmax1 * rmin2
Icp2 = km2Pk_dct(rsum)
Icm2 = km2Pk_dct(rdiff)
Isp3 = km3Pk_dst(rsum)
Ism3 = km3Pk_dst(rdiff)
Icp4 = km4Pk_dct(rsum)
Icm4 = km4Pk_dct(rdiff)
Fp1m2 = -Icp4 + Icm4 - rsum * Isp3 + rdiff * Ism3 + rprod * (Icp2 +
Icm2)
#m1p2: rmin1 & rmax2
rsum = rmin1 + rmax2
rdiff = abs(rmin1 - rmax2)
rprod = rmin1 * rmax2
Icp2 = km2Pk_dct(rsum)
Icm2 = km2Pk_dct(rdiff)
Isp3 = km3Pk_dst(rsum)
Ism3 = km3Pk_dst(rdiff)
Icp4 = km4Pk_dct(rsum)
Icm4 = km4Pk_dct(rdiff)
Fm1p2 = -Icp4 + Icm4 - rsum * Isp3 + rdiff * Ism3 + rprod * (Icp2 +
Icm2)
#m1m2: rmin1 & rmin2
rsum = rmin1 + rmin2
rdiff = abs(rmin1 - rmin2)
rprod = rmin1 * rmin2
Icp2 = km2Pk_dct(rsum)
Icm2 = km2Pk_dct(rdiff)
Isp3 = km3Pk_dst(rsum)
Ism3 = km3Pk_dst(rdiff)
Icp4 = km4Pk_dct(rsum)
Icm4 = km4Pk_dct(rdiff)
Fm1m2 = -Icp4 + Icm4 - rsum * Isp3 + rdiff * Ism3 + rprod * (Icp2 +
Icm2)
#now group everything
Fsum = Fp1p2 - Fp1m2 - Fm1p2 + Fm1m2
Sij[j1, j2] = pref1 * pref2 * Fsum / (4 * pi**2)
return Sij
def Sij_alt(z_arr,
windows,
cosmo_params=default_cosmo_params,
cosmo_Class=None):
# Assert everything as the good type and shape, and find number of redshifts, bins etc
zz = np.asarray(z_arr)
win = np.asarray(windows)
assert zz.ndim == 1, 'z_arr must be a 1-dimensional array'
assert win.ndim == 2, 'windows must be a 2-dimensional array'
nz = len(zz)
nbins = win.shape[0]
assert win.shape[1] == nz, 'windows must have shape (nbins,nz)'
assert zz.min() > 0, 'z_arr must have values > 0'
# If the cosmology is not provided (in the same form as CLASS), run CLASS
if cosmo_Class is None:
cosmo = Class()
dico_for_CLASS = cosmo_params
dico_for_CLASS['output'] = 'mPk'
cosmo.set(dico_for_CLASS)
cosmo.compute()
else:
cosmo = cosmo_Class
h = cosmo.h() #for conversions Mpc/h <-> Mpc
# Define arrays of r(z), k, P(k)...
zofr = cosmo.z_of_r(zz)
comov_dist = zofr[0] #Comoving distance r(z) in Mpc
dcomov_dist = 1 / zofr[1] #Derivative dr/dz in Mpc
dV = comov_dist**2 * dcomov_dist #Comoving volume per solid angle in Mpc^3/sr
growth = np.zeros(nz) #Growth factor
for iz in range(nz):
growth[iz] = cosmo.scale_independent_growth_factor(zz[iz])
keq = 0.02 / h #Equality matter radiation in 1/Mpc (more or less)
klogwidth = 10 #Factor of width of the integration range.
#10 seems ok ; going higher needs to increase nk_fft to reach convergence (fine cancellation issue noted in Lacasa & Grain)
kmin = min(keq, 1. / comov_dist.max()) / klogwidth
kmax = max(keq, 1. / comov_dist.min()) * klogwidth
nk_fft = 2**11 #seems to be enough. Increase to test precision, reduce to speed up.
k_4fft = np.linspace(kmin, kmax,
nk_fft) #linear grid on k, as we need to use an FFT
Deltak = kmax - kmin
Dk = Deltak / nk_fft
Pk_4fft = np.zeros(nk_fft)
for ik in range(nk_fft):
Pk_4fft[ik] = cosmo.pk(k_4fft[ik], 0.) #In Mpc^3
dr_fft = np.linspace(0, nk_fft // 2, nk_fft // 2 + 1) * 2 * pi / Deltak
# Compute necessary FFTs and make interpolation functions
fft0 = np.fft.rfft(Pk_4fft) * Dk
dct0 = fft0.real
dst0 = -fft0.imag
Pk_dct = interp1d(dr_fft, dct0, kind='cubic')
# Compute sigma^2(z1,z2)
sigma2_nog = np.zeros((nz, nz))
#First with P(k,z=0) and z1<=z2
for iz in range(nz):
r1 = comov_dist[iz]
for jz in range(iz, nz):
r2 = comov_dist[jz]
rsum = r1 + r2
rdiff = abs(r1 - r2)
Icp0 = Pk_dct(rsum)
Icm0 = Pk_dct(rdiff)
sigma2_nog[iz, jz] = (Icm0 - Icp0) / (4 * pi**2 * r1 * r2)
#Now fill by symmetry and put back growth functions
sigma2 = np.zeros((nz, nz))
for iz in range(nz):
growth1 = growth[iz]
for jz in range(nz):
growth2 = growth[jz]
sigma2[iz, jz] = sigma2_nog[min(iz, jz),
max(iz, jz)] * growth1 * growth2
# Compute normalisations
Inorm = np.zeros(nbins)
for i1 in range(nbins):
integrand = dV * windows[i1, :]**2
Inorm[i1] = integrate.simps(integrand, zz)
# Compute Sij finally
prefactor = sigma2 * (dV * dV[:, None])
Sij = np.zeros((nbins, nbins))
#For i<=j
for i1 in range(nbins):
for i2 in range(i1, nbins):
integrand = prefactor * (windows[i1, :]**2 *
windows[i2, :, None]**2)
Sij[i1, i2] = integrate.simps(integrate.simps(integrand, zz),
zz) / (Inorm[i1] * Inorm[i2])
#Fill by symmetry
for i1 in range(nbins):
for i2 in range(nbins):
Sij[i1, i2] = Sij[min(i1, i2), max(i1, i2)]
return Sij
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 list(somedict.items())])
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 list(self.scenario.items()):
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(list(self.verbose.items()) + list(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 list(self.scenario.keys()):
# Positive tests of raw cls
output = self.scenario['output']
for elem in output.split():
if elem in list(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 list(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(list(self.verbose.items()) + list(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 list(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 list(self.scenario.keys()):
if 'output' not in list(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 list(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 list(self.scenario.keys()):
if 'output' not in list(self.scenario.keys()):
should_fail = True
# If we ask for Cl's of lensing potential, we must have scalar modes.
if 'output' in list(self.scenario.keys()
) and 'lCl' in self.scenario['output'].split():
if 'modes' in list(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 list(self.scenario.keys()):
if 'modes' in list(self.scenario.keys()
) and self.scenario['modes'].find('s') == -1:
should_fail = True
if 'output' not in list(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 list(self.scenario.keys(
)) and self.scenario['P_k_ini type'].find('inflation') != -1:
if 'modes' not in list(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 list(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 list(ref.items()):
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 list(value.keys()):
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 list(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(
list(self.verbose.items()) + list(self.scenario.items()))
with open(path + '.ini', 'w') as param_file:
for key, value in list(parameters.items()):
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(
list(self.verbose.items()) + list(self.scenario.items()))
with open(path + '.ini', 'w') as param_file:
for key, value in list(parameters.items()):
param_file.write(key + " = " + str(value) + '\n')
start_time = time.time()
cosmo2 = Class()
cosmo2.set(params2)
cosmo2.compute()
print("--- %s seconds ---" % (time.time() - start_time))
pks = []
pks2 = []
# get the pk at each redshift
for j in range(len(zs)):
pk, pk2 = [], []
for ki in k_magnitudes_full[:-1]:
pk.append(cosmo.pk(ki, zs[j]))
pk2.append(cosmo2.pk(ki, zs[j]))
pks.append(pk)
pks2.append(pk2)
os.makedirs('./arrays/', exist_ok=True)
# save arrays
np.savez('./arrays/cdmpk_k{}to{}'.format(k_min, k_max),
ks=k_magnitudes_full,
zs=zs,
pks=pks)
np.savez('./arrays/wdmpk_k{}to{}'.format(k_min, k_max),
ks=k_magnitudes_full,
zs=zs,
pks=pks2)
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")
# Create an instance of the CLASS wrapper
cosmo = Class()
# Set the parameters to the cosmological code
cosmo.set(params_def)
cosmo.compute()
#### Define the linear growth factor and growth rate (growth factor f in class)
h = cosmo.h()
# ~mcu = cosmo.N_ur()
# ~print(mcu)
Plin = np.zeros(len(karray))
Pnonlin = np.zeros(len(karray))
for i, k in enumerate(karray):
Plin[i] = (cosmo.pk_lin(k, z)) # function .pk(k,z)
Pnonlin[i] = (cosmo.pk(k, z)) # function .pk(k,z)
Plin *= h**3
Pnonlin *= h**3
#-------------------------------------------------------------------------
#-------------------------------------------------------------------------
m_1 = 0.02
ocdm_1 = 0.261205 - m_1 * 3 / (93.14 * h**2)
params_1 = {
'output': 'mPk',
'non linear': 'halofit',
'Omega_cdm': ocdm_1,
'N_ncdm': 3,
'N_ur': 0.00441,
'm_ncdm': str(m_1) + ',' + str(m_1) + ',' + str(m_1)
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()
Om = 0.31834
Ocdm = Om - Ob
h = 0.670435
params = {
'output': 'mPk',
"h":h,
"A_s":2.1e-9,
"n_s":0.96191,
"Omega_b":Ob,
"Omega_cdm":Ocdm,
'YHe':0.24755048455476272,#By hand, default value
'P_k_max_h/Mpc':3000.,
'z_max_pk':1.0,
'non linear':'halofit'}
cosmo = Class()
cosmo.set(params)
cosmo.compute()
k = np.logspace(-5, 3, base=10, num=4000) #1/Mpc, apparently
np.savetxt("txt_files/P_files/k.txt", k/h) #h/Mpc now
for i in range(len(zs)):
z = zs[i]
Pmm = np.array([cosmo.pk(ki, z) for ki in k])
Plin = np.array([cosmo.pk_lin(ki, z) for ki in k])
np.savetxt("txt_files/P_files/Pnl_z%.2f.txt"%(z), Pmm*h**3) #Mpc^3/h^3
np.savetxt("txt_files/P_files/Plin_z%.2f.txt"%(z), Plin*h**3) #Mpc^3/h^3
print "Done with z%.2f"%z
#k1 = 0.7*1.028185622909e-5
#k1 = 1.e-6
#z = 0.0
#k1 = 0.1
#print(cosmo.pk(k1,z))
#print(cosmo.pk_lin(k1,z))
k = np.linspace(log(0.0001), log(50), 400)
k = np.exp(k)
testout = [[0 for x in range(42)] for y in range(len(k))]
for i in range(len(k)):
testout[i][0] = k[i]
testout[i][41] = cosmo.pk_lin(k[i], z)
for j in range(40):
# print("j=",j)
testout[i][j + 1] = cosmo.pk(k[i], z)[j]
# testout[i][0] = k[i]
# testout[i][1] = cosmo.pk(k[i],z)[0]
# testout[i][2] = cosmo.pk(k[i],z)[1]
# testout[i][3] = cosmo.pk(k[i],z)[2]
# testout[i][4] = cosmo.pk(k[i],z)[3]
# testout[i][5] = cosmo.pk(k[i],z)[4]
# testout[i][6] = cosmo.pk(k[i],z)[5]
# testout[i][7] = cosmo.pk(k[i],z)[6]
# testout[i][8] = cosmo.pk(k[i],z)[7]
# testout[i][9] = cosmo.pk(k[i],z)[8]
# testout[i][10] = cosmo.pk_lin(k[i],0)
np.savetxt('las_damas_pk_nl.dat', testout)
t2 = time()
print("overall elapsed time=", t2 - t1)
### everything is in units Mpc!
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)
s8 = np.array(results.get_sigma8())
PCLASS = np.array([cosmo.pk_lin(ki * cosmo.h(), 0) for ki in kh])
# plt.loglog(kh, Pmm, '-')
plt.loglog(kh, PCLASS / (cosmo.h())**(-3), '-')
plt.loglog(kh, pk[0, :], '-')
plt.title('LINEAR')
# P(k) Comparison
plt.loglog(kh, np.abs((PCLASS / (cosmo.h())**(-3) - pk[0, :]) / pk[0, :]))
plt.xlabel(r'$k/h$')
plt.ylabel(r'$|\Delta P(k)| / P(k)$')
# Nonlinear P(k) Difference
pars.NonLinear = model.NonLinear_both
results.calc_power_spectra(pars)
kh_nonlin, z_nonlin, pk_nonlin = results.get_matter_power_spectrum(minkh=1e-4,
maxkh=1,
npoints=200)
PCLASS = np.array([cosmo.pk(ki * cosmo.h(), 0) for ki in kh])
plt.loglog(
kh, np.abs(
(PCLASS / (cosmo.h())**(-3) - pk_nonlin[0, :]) / pk_nonlin[0, :]))
plt.xlabel(r'$k/h$')
plt.ylabel(r'$|\Delta P(k)| / P(k)$')
from math import pi
import numpy as np
## ---BEST FIT MATTER POWER SPECTRUM P(k)---
# 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()
# get P(k) at redhsift z=0
kk = np.logspace(-4,np.log10(3),1000)
Pk = []
for k in kk:
Pk.append(LambdaCDM.pk(k,0.)) # function .pk(k,z)
#Upper limit on h
LambdaCDM2 = Class()
LambdaCDM2.set({'omega_b':0.022032,'omega_cdm':0.12038,'h':2.70224,'A_s':2.215e-9,'n_s':0.9619,'tau_reio':0.0925})
LambdaCDM2.set({'output':'tCl,pCl,lCl,mPk','lensing':'yes','P_k_max_1/Mpc':3.0})
LambdaCDM2.compute()
kk2 = np.logspace(-4,np.log10(3),1000)
Pk2 = []
for k in kk2:
Pk2.append(LambdaCDM2.pk(k,0.)) # function .pk(k,z)
#Lower limit on h
LambdaCDM3 = Class()
LambdaCDM3.set({'omega_b':0.022032,'omega_cdm':0.12038,'h':0.16889,'A_s':2.215e-9,'n_s':0.9619,'tau_reio':0.0925})
LambdaCDM3.set({'output':'tCl,pCl,lCl,mPk','lensing':'yes','P_k_max_1/Mpc':3.0})
#k1 = 0.7*1.028185622909e-5
#k1 = 1.e-6
#z = 0.0
#k1 = 0.1
#print(cosmo.pk(k1,z))
#print(cosmo.pk_lin(k1,z))
k = np.linspace(log(0.0001), log(50), 200)
k = np.exp(k)
testout = [[0 for x in range(42)] for y in range(len(k))]
for i in range(len(k)):
testout[i][0] = k[i]
testout[i][41] = cosmo.pk_lin(k[i] * h, z) * h**3
for j in range(40):
# print("j=",j)
testout[i][j + 1] = cosmo.pk(k[i] * h, z)[j] * h**3
# testout[i][0] = k[i]
# testout[i][1] = cosmo.pk(k[i],z)[0]
# testout[i][2] = cosmo.pk(k[i],z)[1]
# testout[i][3] = cosmo.pk(k[i],z)[2]
# testout[i][4] = cosmo.pk(k[i],z)[3]
# testout[i][5] = cosmo.pk(k[i],z)[4]
# testout[i][6] = cosmo.pk(k[i],z)[5]
# testout[i][7] = cosmo.pk(k[i],z)[6]
# testout[i][8] = cosmo.pk(k[i],z)[7]
# testout[i][9] = cosmo.pk(k[i],z)[8]
# testout[i][10] = cosmo.pk_lin(k[i],0)
# np.savetxt('ede_pk_nl_bestfit_z038.dat', testout)
np.savetxt('ede_pk_nl_bestfit_z061.dat', testout)
t2 = time()
print("overall elapsed time=", t2 - t1)
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 = 5.
self.zmax = 4. # should be consistent with fortran code
self.nk_pk = 200
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.}
self.cosmo.set(pars)
self.cosmo.compute()
# 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()
return cl_gg, cl_gy, tll, ng.value
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:]
#
# store P(k) for common k values
#
pkM = []
for k in kvec:
pkM.append(M.pk(k, 0.))
#
# plot P(k)
#
ax_Pk.loglog(kvec,
np.array(pkM),
color=var_color,
alpha=var_alpha,
linestyle='-')
#
# plot C_l^TT
#
ax_TT.semilogx(ll,
clTT * ll * (ll + 1) / twopi,
color=var_color,
alpha=var_alpha,
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(cls):
cls.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(cls.faulty_figs_path):
shutil.rmtree(cls.faulty_figs_path)
os.mkdir(cls.faulty_figs_path)
@classmethod
def tearDownClass(cls):
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()
if CLASS_VERBOSE:
self.verbose = {
'input_verbose': 1,
'background_verbose': 1,
'thermodynamics_verbose': 1,
'perturbations_verbose': 1,
'transfer_verbose': 1,
'primordial_verbose': 1,
'harmonic_verbose': 1,
'fourier_verbose': 1,
'lensing_verbose': 1,
'distortions_verbose': 1,
'output_verbose': 1,
}
else:
self.verbose = {}
self.scenario = {}
def tearDown(self):
self.cosmo.struct_cleanup()
self.cosmo.empty()
self.cosmo = 0
self.cosmo_newt.struct_cleanup()
self.cosmo_newt.empty()
self.cosmo_newt = 0
del self.scenario
def poormansname(self, somedict):
string = "_".join(
[k + '=' + str(v) for k, v in list(somedict.items())])
string = string.replace('/', '%')
string = string.replace(',', '')
string = string.replace(' ', '')
return string
@parameterized.expand(TUPLE_ARRAY,
doc_func=custom_name_func,
custom_name_func=custom_name_func)
@attr('dump_ini_files')
def test_Valgrind(self, inputdict):
"""Dump files"""
self.scenario.update(inputdict)
self.name = self._testMethodName
if self.has_incompatible_input():
return
path = os.path.join(self.faulty_figs_path, self.name)
self.store_ini_file(path)
self.scenario.update({'gauge': 'Newtonian'})
self.store_ini_file(path + 'N')
@parameterized.expand(TUPLE_ARRAY,
doc_func=custom_name_func,
custom_name_func=custom_name_func)
@attr('test_scenario')
def test_scenario(self, inputdict):
"""Test scenario"""
self.scenario.update(inputdict)
self.name = self._testMethodName
self.cosmo.set(
dict(itertools.chain(self.verbose.items(), self.scenario.items())))
cl_dict = {
'tCl': ['tt'],
'lCl': ['pp'],
'pCl': ['ee', 'bb'],
'nCl': ['dd'],
'sCl': ['ll'],
}
# 'lensing' is always set to yes. Therefore, trying to compute 'tCl' or
# 'pCl' will fail except if we also ask for 'lCl'.
if self.has_incompatible_input():
self.assertRaises(CosmoSevereError, self.cosmo.compute)
return
else:
self.cosmo.compute()
self.assertTrue(self.cosmo.state,
"Class failed to go through all __init__ methods")
# 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]:
is_density_cl = (elem == 'nCl' or elem == 'sCl')
if is_density_cl:
cl = self.cosmo.density_cl(100)
else:
cl = self.cosmo.raw_cl(100)
self.assertIsNotNone(cl, "raw_cl returned nothing")
cl_length = np.shape(
cl[cl_type][0])[0] if is_density_cl else np.shape(
cl[cl_type])[0]
self.assertEqual(cl_length, 101,
"raw_cl returned wrong size")
if elem == 'mPk':
pk = self.cosmo.pk(0.1, 0)
self.assertIsNotNone(pk, "pk returned nothing")
# Negative tests of output functions
if not any(
[elem in list(cl_dict.keys()) for elem in output.split()]):
# testing absence of any Cl
self.assertRaises(CosmoSevereError, self.cosmo.raw_cl, 100)
if 'mPk' not in output.split():
# testing absence of mPk
self.assertRaises(CosmoSevereError, self.cosmo.pk, 0.1, 0)
if COMPARE_OUTPUT_REF or COMPARE_OUTPUT_GAUGE:
# Now compute same scenario in Newtonian gauge
self.cosmo_newt.set(
dict(list(self.verbose.items()) + list(self.scenario.items())))
self.cosmo_newt.set({'gauge': 'newtonian'})
self.cosmo_newt.compute()
if COMPARE_OUTPUT_GAUGE:
# Compare synchronous and Newtonian gauge
self.assertTrue(
self.cosmo_newt.state,
"Class failed to go through all __init__ methods in Newtonian gauge"
)
self.compare_output(self.cosmo, "Synchronous", self.cosmo_newt,
'Newtonian', COMPARE_CL_RELATIVE_ERROR_GAUGE,
COMPARE_PK_RELATIVE_ERROR_GAUGE)
if COMPARE_OUTPUT_REF:
# Compute reference models in both gauges and compare
cosmo_ref = classyref.Class()
cosmo_ref.set(self.cosmo.pars)
cosmo_ref.compute()
status = self.compare_output(cosmo_ref, "Reference", self.cosmo,
'Synchronous',
COMPARE_CL_RELATIVE_ERROR,
COMPARE_PK_RELATIVE_ERROR)
assert status, 'Reference comparison failed in Synchronous gauge!'
cosmo_ref = classyref.Class()
cosmo_ref.set(self.cosmo_newt.pars)
cosmo_ref.compute()
self.compare_output(cosmo_ref, "Reference", self.cosmo_newt,
'Newtonian', COMPARE_CL_RELATIVE_ERROR,
COMPARE_PK_RELATIVE_ERROR)
assert status, 'Reference comparison failed in Newtonian gauge!'
def has_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 list(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 list(self.scenario.keys()):
if 'output' not in list(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 list(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 list(self.scenario.keys()):
if 'output' not in list(self.scenario.keys()):
should_fail = True
# If we ask for Cl's of lensing potential, number counts or cosmic shear, we must have scalar modes.
# The same applies to density and velocity transfer functions and the matter power spectrum:
if 'output' in self.scenario and 'modes' in self.scenario and self.scenario[
'modes'].find('s') == -1:
requested_output_types = set(self.scenario['output'].split())
for scalar_output_type in [
'lCl', 'nCl', 'dCl', 'sCl', 'mPk', 'dTk', 'mTk', 'vTk'
]:
if scalar_output_type in requested_output_types:
should_fail = True
break
# If we specify initial conditions (for scalar modes), we must have
# perturbations and scalar modes.
if 'ic' in list(self.scenario.keys()):
if 'modes' in list(self.scenario.keys()
) and self.scenario['modes'].find('s') == -1:
should_fail = True
if 'output' not in list(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 list(self.scenario.keys(
)) and self.scenario['P_k_ini type'].find('inflation') != -1:
if 'modes' not in list(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 list(self.scenario.keys()
) and self.scenario['ic'].find('i') != -1:
should_fail = True
return should_fail
def compare_output(self, reference, reference_name, candidate,
candidate_name, rtol_cl, rtol_pk):
status_pass = True
for elem in ['raw_cl', 'lensed_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 list(ref.items()):
if key != 'ell':
# 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 list(value.keys()):
try:
np.testing.assert_allclose(
value[subkey],
to_test[key][subkey],
rtol=rtol_cl,
atol=COMPARE_CL_ABSOLUTE_ERROR)
except AssertionError:
self.cl_faulty_plot(
elem + "_" + key, value[subkey][2:],
reference_name,
to_test[key][subkey][2:],
candidate_name, rtol_cl)
except TypeError:
self.cl_faulty_plot(
elem + "_" + key, value[subkey][2:],
reference_name,
to_test[key][subkey][2:],
candidate_name, rtol_cl)
else:
try:
np.testing.assert_allclose(
value,
to_test[key],
rtol=rtol_cl,
atol=COMPARE_CL_ABSOLUTE_ERROR)
except (AssertionError, TypeError) as e:
self.cl_faulty_plot(elem + "_" + key,
value[2:], reference_name,
to_test[key][2:],
candidate_name, rtol_cl)
status_pass = False
# 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:],
reference_name,
to_test[key][2:],
candidate_name, rtol_cl)
status_pass = False
if 'output' in list(self.scenario.keys()):
if self.scenario['output'].find('mPk') != -1:
# testing equality of Pk
k = np.logspace(-2, log10(self.scenario['P_k_max_1/Mpc']), 50)
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=rtol_pk,
atol=COMPARE_PK_ABSOLUTE_ERROR)
except AssertionError:
self.pk_faulty_plot(k, reference_pk, reference_name,
candidate_pk, candidate_name, rtol_pk)
status_pass = False
return status_pass
def store_ini_file(self, path):
parameters = dict(
list(self.verbose.items()) + list(self.scenario.items()))
with open(path + '.ini', 'w') as param_file:
param_file.write('# ' + str(parameters) + '\n')
if len(parameters) == 0:
# CLASS complains if the .ini file does not do anything.
param_file.write('write warnings = yes\n')
for key, value in list(parameters.items()):
param_file.write(key + " = " + str(value) + '\n')
def cl_faulty_plot(self, cl_type, reference, reference_name, candidate,
candidate_name, rtol):
path = os.path.join(self.faulty_figs_path, self.name)
fig, axes = plt.subplots(2, 1, sharex=True)
ell = np.arange(max(np.shape(candidate))) + 2
factor = ell * (ell + 1) / (2 *
np.pi) if cl_type[-2:] != 'pp' else ell**5
axes[0].plot(ell, factor * reference, label=reference_name)
axes[0].plot(ell, factor * candidate, label=candidate_name)
axes[1].semilogy(ell,
100 * abs(candidate / reference - 1),
label=cl_type)
axes[1].axhline(y=100 * rtol, color='k', ls='--')
axes[-1].set_xlabel(r'$\ell$')
if cl_type[-2:] == 'pp':
axes[0].set_ylabel(r'$\ell^5 C_\ell^\mathrm{{{_cl_type}}}$'.format(
_cl_type=cl_type[-2:].upper()))
else:
axes[0].set_ylabel(
r'$\ell(\ell + 1)/(2\pi)C_\ell^\mathrm{{{_cl_type}}}$'.format(
_cl_type=cl_type[-2:].upper()))
axes[1].set_ylabel('Relative error [%]')
for ax in axes:
ax.legend(loc='upper right')
fig.tight_layout()
fname = '{}_{}_{}_vs_{}.pdf'.format(path, cl_type, reference_name,
candidate_name)
fig.savefig(fname, bbox_inches='tight')
plt.close(fig)
# Store parameters (contained in self.scenario) to text file
self.store_ini_file(path)
def pk_faulty_plot(self, k, reference, reference_name, candidate,
candidate_name, rtol):
path = os.path.join(self.faulty_figs_path, self.name)
fig, axes = plt.subplots(2, 1, sharex=True)
axes[0].loglog(k, k**1.5 * reference, label=reference_name)
axes[0].loglog(k, k**1.5 * candidate, label=candidate_name)
axes[0].legend(loc='upper right')
axes[1].loglog(k, 100 * np.abs(candidate / reference - 1))
axes[1].axhline(y=100 * rtol, color='k', ls='--')
axes[-1].set_xlabel(r'$k\quad [\mathrm{Mpc}^{-1}]$')
axes[0].set_ylabel(r'$k^\frac{3}{2}P(k)$')
axes[1].set_ylabel(r'Relative error [%]')
fig.tight_layout()
fname = path + '_pk_{}_vs_{}.pdf'.format(reference_name,
candidate_name)
fig.savefig(fname, bbox_inches='tight')
plt.close(fig)
# Store parameters (contained in self.scenario) to text file
self.store_ini_file(path)
# In[ ]:
plt.savefig('warmup_cltt.pdf')
# In[ ]:
# get P(k) at redhsift z=0
import numpy as np
kk = np.logspace(-4,np.log10(3),1000) # k in h/Mpc
Pk = [] # P(k) in (Mpc/h)**3
h = LambdaCDM.h() # get reduced Hubble for conversions to 1/Mpc
for k in kk:
Pk.append(LambdaCDM.pk(k*h,0.)*h**3) # function .pk(k,z)
# In[ ]:
# plot P(k)
plt.figure(2)
plt.xscale('log');plt.yscale('log');plt.xlim(kk[0],kk[-1])
plt.xlabel(r'$k \,\,\,\, [h/\mathrm{Mpc}]$')
plt.ylabel(r'$P(k) \,\,\,\, [\mathrm{Mpc}/h]^3$')
plt.plot(kk,Pk,'b-')
# In[ ]:
plt.savefig('warmup_pk.pdf')
print(cl_gg_tmp)
print((cl_gg - cl_gg_tmp) / cl_gg)
plt.plot(ells, cl_gg)
plt.plot(ells, cl_gg_tmp)
plt.xscale('log')
plt.yscale('log')
plt.show()
quit()
for i in range(7):
z_test = z_templates['ztmp%d' % i]
NL = ccl.nonlin_matter_power(cosmo_ccl, ks * h, a=1. / (1 + z_test))
print(z_test, h)
class_ks = np.logspace(-5, np.log10(1), 1000)
class_pk = np.array([class_cosmo.pk(ki, z_test) for ki in class_ks])
lk = Pk_a_ij['lk_arr']
Pk = Pk_a_ij['1_1'][i, :]
plt.figure(i + 1)
'''
plt.semilogy(lk, NL, label="CCL")
plt.semilogy(lk, Pk, label="N-body")
plt.plot(lk, np.interp(lk, np.log(class_ks), class_pk), label="CLASS")
'''
plt.plot(lk, np.ones(len(lk)), 'k--')
plt.plot(lk, Pk / NL, label="N-body/CCL")
try:
Pk_11 = np.load("Pk_11_" + par + "_ztmp%d.npy" % i) / h**3
plt.plot(lk, Pk_11 / NL, label="N-body der/CCL")
except:
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()
