phi = one_time['phi']
psi = one_time['psi']
theta_b = one_time['t_b']
# compute related quantitites
R = 3. / 4. * M.Omega_b() / M.Omega_g() / (
1 + z_rec) # R = 3/4 * (rho_b/rho_gamma) at z_rec
zero_point = -(1. + R) * psi # zero point of oscillations: -(1.+R)*psi
#
# get Theta0 oscillation amplitude (for vertical scale of plot)
#
Theta0_amp = max(Theta0.max(), -Theta0.min())
#
# use table of background quantitites to find the wavenumbers corresponding to
# Hubble crossing (k = 2 pi a H), sound horizon crossing (k = 2pi / rs)
#
background = M.get_background() # load background table
#print background.viewkeys()
#
background_tau = background[
'conf. time [Mpc]'] # read confromal times in background table
background_z = background['z'] # read redshift
background_kh = 2. * math.pi * background['H [1/Mpc]'] / (
1. + background['z']
) / M.h() # read kh = 2pi aH = 2pi H/(1+z) converted to [h/Mpc]
background_ks = 2. * math.pi / background['comov.snd.hrz.'] / M.h(
) # read ks = 2pi/rs converted to [h/Mpc]
#
# define interpolation functions; we want the value of tau when the argument is equal to 2pi
#
kh_at_tau = interp1d(background_tau, background_kh)
ks_at_tau = interp1d(background_tau, background_ks)
'attractor_ic_scf': 'no',
'write background':'yes',
'compute damping scale':'yes'}
try:
cosmo.set(params) # Set the parameters to the cosmological code
cosmo.compute() # solve physics
m_axion = 10**cosmo.log10_m_axion()*h*100/_c_
print "m_axion %f"%(m_axion)
# k_res_analytical = 0.79*m_axion*Theta_initial[j]*(10**ac[i])
k_res_analytical = 0.79*m_axion*Theta_initial[j]*(10**ac[i])
one_time = cosmo.get_transfer(0) # transfer functions at each time tau
k = one_time['k (h/Mpc)']
delta_phi_scf = np.abs(one_time['delta_phi_scf']) ##evaluated today
k_of_delta = interp1d(delta_phi_scf,k)
background = cosmo.get_background() # load background table
background_tau = background['conf. time [Mpc]'] # read conformal times in background table
background_z = background['z'] # read redshift
background_Om_scf = background['(.)Omega_scf'] # read redshift
background_z_at_tau = interp1d(background_tau,background_z)
phi_enveloppe = Theta_initial[j]*10**cosmo.log10_f_axion()*(2/(10**ac[i]))**(-3.*(1+wn)/2/n_axion) ##evaluated today
delta_max = np.sqrt(delta_phi_scf*delta_phi_scf*A_s*(k/K_star)**(n_s-1))/(phi_enveloppe)
print max(delta_max), np.where(delta_max==max(delta_max))
k_res_numerical = k[np.where(delta_max==max(delta_max))]*h ##evaluated today
print "k_res analytical %f Mpc-1, k_res numerical %f Mpc-1"%(k_res_analytical,k_res_numerical)
except CosmoComputationError: # this happens when CLASS fails
print "bug!"
k_res_numerical = 0
k_nl_scf[i][j] = 100 #arbitrary large number: bug
z_nl_scf[i][j] = 0 #arbitrary large number: bug
Omega_nl_scf[i][j] = 1e-30 #arbitrary small number
from scipy.interpolate import interp1d
import math
common_settings = {# we need to set the output field to something although
# the really releveant outpout here will be set with 'k_output_values'
'output':'mPk',
# value of k we want to polot in [1/Mpc]
# 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 options and settings
'compute damping scale':'yes', # needed to output the time of damping scale crossing
'gauge':'newtonian'}
M = Class()
M.set(common_settings)
M.compute()
growth = M.get_background()['gr.fac. D']
zs = M.get_background()['z']
scalefactor = 1/zs
#plt.semilogx(zs, growth,label = "D(z)")
plt.semilogx(scalefactor, growth,label = "D(a)")
plt.legend()
plt.show()
'adptative_stepsize': 100,
'scf_evolve_as_fluid': 'no'
})
# 2.258053e-02 1.298650e-01 9.880137e-01 2.176655e-09 6.833666e-02 1.041246e+00 -3.736778e+00 4.717743e-09 2.791761e+00
M.compute()
#
# get Cls
#
# clM = M.raw_cl(2500
clM = M.lensed_cl(2500)
ll = clM['ell'][2:]
clTT = clM['tt'][2:]
clTE = clM['te'][2:]
clEE = clM['ee'][2:]
bg = M.get_background()
Omega_scf = bg['(.)Omega_scf']
H = bg['H [1/Mpc]']
Da = bg['ang.diam.dist.']
z = bg['z']
tau = bg['conf. time [Mpc]']
tau_interp = interp1d(z, tau)
tau_0 = tau_interp(0)
derived = M.get_current_derived_parameters(['z_rec', 'tau_rec'])
tau_rec = derived['tau_rec']
for j in range(len(Omega_scf)):
if Omega_scf[j] > 0.01:
k = H[j] / (1 + z[j])
# l.append(k*Da[j])
l.append(k * (tau_0 - tau_rec))
print(450. / (tau_0 - tau_rec))
# define conformal time sampling array # times = M.get_current_derived_parameters(['tau_rec','conformal_age']) tau_rec=times['tau_rec'] tau_0 = times['conformal_age'] tau1 = np.logspace(math.log10(tau_ini),math.log10(tau_rec),tau_num_early) tau2 = np.logspace(math.log10(tau_rec),math.log10(tau_0),tau_num_late)[1:] tau2[-1] *= 0.999 # this tiny shift avoids interpolation errors tau = np.concatenate((tau1,tau2)) tau_num = len(tau) # # use table of background and thermodynamics quantitites to define some functions # returning some characteristic scales # (of Hubble crossing, sound horizon crossing, etc.) at different time # background = M.get_background() # load background table #print background.viewkeys() thermodynamics = M.get_thermodynamics() # load thermodynamics table #print thermodynamics.viewkeys() # background_tau = background['conf. time [Mpc]'] # read conformal times in background table background_z = background['z'] # read redshift background_aH = 2.*math.pi*background['H [1/Mpc]']/(1.+background['z'])/M.h() # read 2pi * aH in [h/Mpc] background_ks = 2.*math.pi/background['comov.snd.hrz.']/M.h() # read 2pi/(comoving sound horizon) in [h/Mpc] background_rho_m_over_r = (background['(.)rho_b']+background['(.)rho_cdm']) /(background['(.)rho_g']+background['(.)rho_ur']) # read rho_r / rho_m (to find time of equality) background_rho_l_over_m = background['(.)rho_lambda'] /(background['(.)rho_b']+background['(.)rho_cdm']) # read rho_m / rho_lambda (to find time of equality) thermodynamics_tau = thermodynamics['conf. time [Mpc]'] # read confromal times in thermodynamics table thermodynamics_kd = 2.*math.pi/thermodynamics['r_d']/M.h() # read 2pi(comoving diffusion scale) in [h/Mpc] # # define a bunch of interpolation functions based on previous quantities #
class Binning():
def __init__(self, fname, outdir='./'):
self._cosmo = Class()
self._fname = fname
self._outdir = outdir
self._set_default_values()
def _set_full_filenames(self, filesuffixes):
"""
Return a list with the fullnames of the others, increasing their number
in case they exist
Additionally, set the full file names, of self._fparamsname and
self.fshootname.
"""
fullfilenames = []
for suffix in filesuffixes + ['params', 'shooting']:
fullfilenames.append(
os.path.join(self._outdir, self._fname + '-' + suffix))
i = 0
bools = [True] * len(fullfilenames)
while 1:
for n, f in enumerate(fullfilenames):
bools[n] = os.path.exists(f + '-%s.txt' % i)
if True not in bools:
break
i += 1
self._fparamsname = fullfilenames[-2] + '-%s.txt' % i
self._fshootname = fullfilenames[-1] + '-%s.txt' % i
return [f + '-%s.txt' % i for f in fullfilenames[:-2]]
def _set_default_values(self):
"""
Set default values of parameters lists.
"""
self.params_smg = []
self.params_2_smg = []
self.h = []
self.Omega_cdm = []
self.gravity_model = []
self._params = {
"Omega_Lambda": 0,
"Omega_fld": 0,
"Omega_smg": -1,
'output': 'mPk', #
'z_max_pk': 1000
} # Added for relative errors in f.
self._computed = False
self._path = []
self._binType = ''
def set_Pade(self,
n_num,
m_den,
xvar='a',
xReverse=False,
accuracy=1e-3,
increase=False,
maxfev=0):
"""
Set what Pade polynomial orders, temporal variable and its ordering use.
"""
self.reset()
self._PadeOrder = [n_num, m_den]
self._Pade_xvar = xvar
self._Pade_xReverse = xReverse
self._Pade_maxfev = maxfev
self._Pade_increase = increase
self._Pade_accuracy = accuracy
self._binType = 'Pade'
def set_fit(self,
fit_function,
n_coeffs,
variable_to_fit,
fit_function_label='',
z_max_pk=1000,
bounds=(-np.inf, np.inf),
p0=[],
xvar='ln(1+z)'):
"""
Set the fitting_function and the number of coefficients.
variable_to_fit must be one of 'F'or 'w'.
fit_function_label will be written in the header of fit files.
"""
self.reset()
self._fit_function = fit_function
self._n_coeffs = n_coeffs
self._list_variables_to_fit = ['F', 'w', 'logRho', 'logX', 'X']
if variable_to_fit in self._list_variables_to_fit:
self._variable_to_fit = variable_to_fit
else:
raise ValueError('variable_to_fit must be one of {}'.format(
self._list_variables_to_fit))
self._fit_function_label = fit_function_label
self._binType = 'fit'
self._fFitname = self._set_full_filenames(['fit-' + variable_to_fit
])[0]
self._params.update({'z_max_pk': z_max_pk})
self._fit_bounds = bounds
self._p0 = p0
list_fit_xvar = ['ln(1+z)', 'lna', 'a', '(1-a)']
if xvar in list_fit_xvar:
self._fit_xvar = xvar
else:
raise ValueError('xvar must be one of {}'.format(list_fit_xvar))
def set_bins(self, zbins, abins):
"""
Set what bins to use and reset to avoid confusions.
"""
self.reset()
self._zbins = zbins
self._abins = abins
self._binType = 'bins'
self._fwzname, self._fwaname = self._set_full_filenames(
['wz-bins', 'wa-bins'])
def _read_from_file(self, path):
"""
Return params for class from files used in quintessence Marsh.
"""
with open(path) as f:
f.readline()
header = f.readline().split()[3:] # Remove '#', 'w0', and 'wa'
columns = np.loadtxt(path, unpack=True)[2:] # Remove columns w0, wa
for index_h, head in enumerate(header):
if head[-1] == 'h':
break
self.params_smg = zip(*columns[:index_h])
self.params_2_smg = [
list(row[~np.isnan(row)])
for row in np.array(zip(*columns[index_h + 2:]))
]
self.h = columns[index_h]
self.Omega_cdm = columns[index_h + 1]
self.gravity_model = os.path.basename(path).split('-')[0]
def _params_from_row(self, row):
"""
Set parameters.
"""
params = self._params
params.update({
'parameters_smg': str(self.params_smg[row]).strip('[()]'),
'h': self.h[row],
'Omega_cdm': self.Omega_cdm[row],
'gravity_model': self.gravity_model
})
if len(self.params_2_smg):
params.update({
'parameters_2_smg':
str(self.params_2_smg[row]).strip('[()]')
})
return params
def compute_bins(self, params):
"""
Compute the w_i bins for the model with params.
"""
wzbins = np.empty(len(self._zbins))
wabins = np.empty(len(self._abins))
self._params = params
self._cosmo.set(params)
try:
self._cosmo.compute()
for n, z in enumerate(self._zbins):
wzbins[n] = self._cosmo.w_smg(z)
for n, a in enumerate(self._abins):
wabins[n] = self._cosmo.w_smg(1. / a - 1.)
shoot = self._cosmo.get_current_derived_parameters(
['tuning_parameter'])['tuning_parameter']
except Exception as e:
self._cosmo.struct_cleanup()
self._cosmo.empty()
raise e
self._cosmo.struct_cleanup()
self._cosmo.empty()
return wzbins, wabins, shoot
def _compute_common_init(self, params):
"""
Common first steps for compute methods
"""
self._params.update(params)
self._cosmo.set(self._params)
try:
self._cosmo.compute()
b = self._cosmo.get_background()
shoot = self._cosmo.get_current_derived_parameters(
['tuning_parameter'])['tuning_parameter']
except Exception as e:
self._cosmo.struct_cleanup()
self._cosmo.empty()
raise e
return b, shoot
def _fit(self, X, Y):
"""
Fits self._fit_function to X, Y, with self._n_coeffs.
"""
return wicm.fit(self._fit_function,
X,
Y,
self._n_coeffs,
bounds=self._fit_bounds,
p0=self._p0)
def _get_fit_xvar_and_log1Plusz(self, z):
"""
Return the X array to fit and log(1+z)
"""
if self._fit_xvar == 'ln(1+z)':
X = np.log(z + 1)
lna = X
elif self._fit_xvar == 'lna':
X = -np.log(z + 1)
lna = -X
elif self._fit_xvar == 'a':
X = 1. / (1 + z)
lna = np.log(z + 1)
elif self._fit_xvar == '(1-a)':
X = (z / (1 + z))
lna = np.log(z + 1)
return X, lna
def compute_fit_coefficients_for_F(self, params):
"""
Returns the coefficients of the polynomial fit of f(a) = \int w and the
maximum and minimum residual in absolute value.
"""
b, shoot = self._compute_common_init(params)
# Compute the exact \int dlna a
###############################
z, w = b['z'], b['w_smg']
Fint = []
lna = -np.log(1 + z)[::-1]
for i in lna:
Fint.append(integrate.trapz(w[::-1][lna >= i], lna[lna >= i]))
Fint = np.array(Fint)
#####
zlim = self._params['z_max_pk']
# Note that lna is log(1+z). I used this name because is more convenient
X, lna = self._get_fit_xvar_and_log1Plusz(z[z <= zlim])
#####################
zTMP = z[z <= zlim]
Y1 = Fint[::-1][z < zlim] # Ordered as in CLASS
#####################
# Fit to fit_function
#####################
popt1, yfit1 = self._fit(X, Y1)
# Obtain max. rel. dev. for DA and f.
#####################
rhoDE_fit = b['(.)rho_smg'][-1] * np.exp(-3 * yfit1) * (
1 + zTMP)**3 ###### CHANGE WITH CHANGE OF FITTED THING
Xw_fit, w_fit = wicm.diff(lna, yfit1)
w_fit = -interp1d(
Xw_fit, w_fit, bounds_error=False, fill_value='extrapolate')(lna)
DA_reldev, f_reldev = self._compute_maximum_relative_error_DA_f(
rhoDE_fit, w_fit)
# Free structures
###############
self._cosmo.struct_cleanup()
self._cosmo.empty()
return np.concatenate([popt1, [DA_reldev, f_reldev]]), shoot
def compute_fit_coefficients_for_logX(self, params):
"""
Returns the coefficients of the polynomial fit of log(rho/rho_0) = -3
\int dlna (w+1) and the maximum and minimum residual in absolute value.
"""
b, shoot = self._compute_common_init(params)
# Compute the exact -3 \int dlna (w + 1)
###############################
z = b['z']
logX = np.log(b['(.)rho_smg'] / b['(.)rho_smg'][-1])
#####
zlim = self._params['z_max_pk']
# Note that lna is log(1+z). I used this name because is more convenient
X, lna = self._get_fit_xvar_and_log1Plusz(z[z <= zlim])
#####################
Y1 = logX[z <= zlim]
#####################
# Fit to fit_function
#####################
popt1, yfit1 = self._fit(X, Y1)
# Obtain max. rel. dev. for DA and f.
#####################
rhoDE_fit = b['(.)rho_smg'][-1] * np.exp(
yfit1) ###### CHANGE WITH CHANGE OF FITTED THING
Xw_fit, ThreewPlus1 = wicm.diff(lna, yfit1)
w_fit = ThreewPlus1 / 3. - 1 # The minus sign is taken into account by the CLASS ordering.
w_fit = interp1d(Xw_fit,
w_fit,
bounds_error=False,
fill_value='extrapolate')(lna)
DA_reldev, f_reldev = self._compute_maximum_relative_error_DA_f(
rhoDE_fit, w_fit)
# Free structures
###############
self._cosmo.struct_cleanup()
self._cosmo.empty()
return np.concatenate([popt1, [DA_reldev, f_reldev]]), shoot
def compute_fit_coefficients_for_X(self, params):
"""
Returns the coefficients of the polynomial fit of rho/rho_0 = exp[-3
\int dlna (w+1)] and the maximum and minimum residual in absolute value.
"""
b, shoot = self._compute_common_init(params)
# Compute the exact -3 \int dlna (w + 1)
###############################
z = b['z']
Y = b['(.)rho_smg'] / b['(.)rho_smg'][-1]
#####
zlim = self._params['z_max_pk']
# Note that lna is log(1+z). I used this name because is more convenient
X, lna = self._get_fit_xvar_and_log1Plusz(z[z <= zlim])
#####################
Y1 = Y[z <= zlim]
#####################
# Fit to fit_function
#####################
popt1, yfit1 = self._fit(X, Y1)
# Obtain max. rel. dev. for DA and f.
#####################
rhoDE_fit = b['(.)rho_smg'][
-1] * yfit1 ###### CHANGE WITH CHANGE OF FITTED THING
Xw_fit, diff = wicm.diff(lna, yfit1)
diff = interp1d(Xw_fit,
diff,
bounds_error=False,
fill_value='extrapolate')(lna)
ThreewPlus1 = diff / yfit1
w_fit = ThreewPlus1 / 3. - 1 # The minus sign is taken into account by the CLASS ordering.
DA_reldev, f_reldev = self._compute_maximum_relative_error_DA_f(
rhoDE_fit, w_fit)
# Free structures
###############
self._cosmo.struct_cleanup()
self._cosmo.empty()
return np.concatenate([popt1, [DA_reldev, f_reldev]]), shoot
def compute_fit_coefficients_for_logRho(self, params):
"""
Returns the coefficients of the fit of ln(rho_de) and the maximum and
minimum residual in absolute value.
"""
b, shoot = self._compute_common_init(params)
# Compute the exact log(rho)
###############################
z = b['z']
logX = np.log(b['(.)rho_smg'])
#####
zlim = self._params['z_max_pk']
# Note that lna is log(1+z). I used this name because is more convenient
X, lna = self._get_fit_xvar_and_log1Plusz(z[z <= zlim])
#####################
Y1 = logX[z <= zlim]
#####################
# Fit to fit_function
#####################
popt1, yfit1 = self._fit(X, Y1)
# Obtain max. rel. dev. for DA and f.
#####################
rhoDE_fit = np.exp(yfit1) ###### CHANGE WITH CHANGE OF FITTED THING
Xw_fit, ThreewPlus1 = wicm.diff(lna, yfit1 - yfit1[-1])
w_fit = ThreewPlus1 / 3. - 1 # The minus sign is taken into account by the CLASS ordering.
w_fit = interp1d(Xw_fit,
w_fit,
bounds_error=False,
fill_value='extrapolate')(lna)
DA_reldev, f_reldev = self._compute_maximum_relative_error_DA_f(
rhoDE_fit, w_fit)
# Free structures
###############
self._cosmo.struct_cleanup()
self._cosmo.empty()
return np.concatenate([popt1, [DA_reldev, f_reldev]]), shoot
def compute_fit_coefficients_for_w(self, params):
"""
Returns the coefficients of the polynomial fit of f(a) = \int w and the
maximum and minimum residual in absolute value.
"""
b, shoot = self._compute_common_init(params)
# Compute the exact \int dlna a
###############################
z, w = b['z'], b['w_smg']
zlim = self._params['z_max_pk']
# Note that lna is log(1+z). I used this name because is more convenient
X, lna = self._get_fit_xvar_and_log1Plusz(z[z <= zlim])
#####################
zTMP = z[z <= zlim]
Y1 = w[z <= zlim]
# Fit to fit_function
#####################
popt1, yfit1 = self._fit(X, Y1)
# Obtain max. rel. dev. for DA and f.
#####################
Fint = []
lna = -np.log(1 + zTMP)[::-1]
for i in lna:
Fint.append(integrate.trapz(yfit1[::-1][lna >= i], lna[lna >= i]))
Fint = np.array(Fint[::-1])
rhoDE_fit = b['(.)rho_smg'][-1] * np.exp(
-3 * Fint) * (1 + zTMP)**3 # CHANGE WITH CHANGE OF FITTED THING
# TODO: needed?
# Xw_fit, w_fit = X, yfit1
# w_fit = interp1d(Xw_fit, w_fit, bounds_error=False, fill_value='extrapolate')(X)
w_fit = yfit1
DA_reldev, f_reldev = self._compute_maximum_relative_error_DA_f(
rhoDE_fit, w_fit)
# Free structures
###############
self._cosmo.struct_cleanup()
self._cosmo.empty()
return np.concatenate([popt1, [DA_reldev, f_reldev]]), shoot
def _compute_maximum_relative_error_DA_f(self, rhoDE_fit, w_fit):
"""
Return the relative error for the diameter angular distance and the
growth factor, f.
rhoDE_fit = array
wfit = interp1d(w)
"""
b = self._cosmo.get_background()
# Compute the exact growth rate
#####################
z_max_pk = self._params['z_max_pk']
zlim = z_max_pk
z, w = b['z'], b['w_smg']
zTMP = z[z <= zlim]
rhoM = (b['(.)rho_b'] + b['(.)rho_cdm'])
rhoR = (b['(.)rho_g'] + b['(.)rho_ur'])
DA = b['ang.diam.dist.']
OmegaDEwF_exact = interp1d(z[z <= z_max_pk],
(b['(.)rho_smg'] / b['(.)rho_crit'] *
w)[z <= z_max_pk])
OmegaMF = interp1d(z[z <= z_max_pk],
(rhoM / b['(.)rho_crit'])[z <= z_max_pk])
time_boundaries = [z[z <= z_max_pk][0], z[z <= z_max_pk][-1]]
# Use LSODA integrator as some solutions were wrong with RK45 and OK
# with this.
f = integrate.solve_ivp(
cosmo_extra.fprime(OmegaDEwF_exact, OmegaMF),
time_boundaries,
[cosmo_extra.growthrate_at_z(self._cosmo, z_max_pk)],
method='LSODA',
dense_output=True)
# Compute D_A for fitted model
################
H_fit = np.sqrt(rhoM[z <= zlim] + rhoR[z <= zlim] + rhoDE_fit)
DA_fit = cosmo_extra.angular_distance(z[z <= zlim],
H_fit[zTMP <= zlim])
# Compute the growth rate for fitted model
###############
OmegaMF_fit = interp1d(
zTMP, 1 - rhoDE_fit / H_fit**2 - rhoR[z <= zlim] /
H_fit**2) ####### THIS FITS OBSERVABLES CORRECTLY
# OmegaMF_fit = interp1d(zTMP, rhoM[z<=zlim]/H_fit**2) ####### THIS FITS OBSERVABLES CORRECTLY
OmegaDEwF_fit = interp1d(zTMP, rhoDE_fit / H_fit**2 * w_fit)
f_fit = integrate.solve_ivp(
cosmo_extra.fprime(OmegaDEwF_fit,
OmegaMF_fit), [zTMP[0], zTMP[-1]],
[cosmo_extra.growthrate_at_z(self._cosmo, zTMP[0])],
method='LSODA',
dense_output=True)
# Obtain rel. deviations.
################
# Remove close to 0 points as rel.dev diverges. z = 0.05 is the lowest
# redshift observed and is done in BOSS survey. arXiv: 1308.4164
# DA_reldev = max(np.abs(DA_fit[zTMP>=0.04]/DA[ (z>=0.04) & (z<=zlim)] - 1))
DA_reldev = max(np.abs(DA_fit / DA[z <= zlim] - 1))
f_reldev = max(np.abs(f_fit.sol(zTMP)[0] / f.sol(zTMP)[0] - 1))
return DA_reldev, f_reldev
def compute_Pade_coefficients(self, params):
"""
Returns the Pade coefficients for w computed from params and the maximum
and minimum residual in absolute value.
"""
self._params = params
self._cosmo.set(params)
try:
self._cosmo.compute()
b = self._cosmo.get_background()
shoot = self._cosmo.get_current_derived_parameters(
['tuning_parameter'])['tuning_parameter']
except Exception as e:
self._cosmo.struct_cleanup()
self._cosmo.empty()
raise e
self._cosmo.struct_cleanup()
self._cosmo.empty()
xDict = {
'z': b['z'],
'z+1': b['z'] + 1,
'a': 1. / (b['z'] + 1),
'log(a)': -np.log(b['z'] + 1),
'log(z+1)': np.log(b['z'] + 1)
}
X = xDict[self._Pade_xvar]
w = b['w_smg']
if self._Pade_xReverse:
X = X[::-1]
w = w[::-1]
PadeOrder = np.array(self._PadeOrder)
if not self._Pade_increase:
reduceOrder = [[0, 0], [1, 0], [0, 1], [2, 0], [2, 1], [3, 1]]
orderList = PadeOrder - reduceOrder
else:
orderList = [[1, 1], [2, 0], [3, 0], [2, 1], [2, 2], [3,
1], [4, 0],
[2, 3], [3, 2], [4, 1], [5, 0], [3, 3], [4,
2], [5, 1],
[3, 4], [4, 3], [5, 2], [3, 5], [4, 4], [5, 3],
[4, 5], [5, 4], [5, 5]]
r = np.array([np.inf])
for order in orderList:
# Increase order of Pade up to [5/5].
try:
padeCoefficientsTMP, padeFitTMP = fit_pade(
X, w, *order, maxfev=self._Pade_maxfev)
rTMP = np.abs(padeFitTMP / w - 1.)
if self._Pade_increase and (np.max(rTMP) >
self._Pade_accuracy):
if np.max(rTMP) < np.max(r):
padeCoefficients = padeCoefficientsTMP
r = rTMP
continue
else:
padeCoefficients = padeCoefficientsTMP
r = rTMP
break
except Exception as e:
if (order == orderList[-1]) and (len(r) == 1):
raise e
continue
zeros = (PadeOrder - order)
numCoefficients = np.append(padeCoefficients[:order[0] + 1],
[0.] * zeros[0])
denCoefficients = np.append(padeCoefficients[order[0] + 1:],
[0.] * zeros[1])
padeCoefficients = np.concatenate([numCoefficients, denCoefficients])
return np.concatenate([padeCoefficients, [np.min(r),
np.max(r)]]), shoot
def compute_bins_from_params(self, params_func, number_of_rows):
"""
Compute the w_i bins for the models given by the function
params_func iterated #iterations.
"""
self._create_output_files()
wzbins = []
wabins = []
params = []
shoot = []
for row in range(number_of_rows):
sys.stdout.write("{}/{}\n".format(row + 1, number_of_rows))
params_tmp = params_func().copy()
try:
wzbins_tmp, wabins_tmp, shoot_tmp = self.compute_bins(
params_tmp)
wzbins.append(wzbins_tmp)
wabins.append(wabins_tmp)
params.append(params_tmp)
shoot.append(shoot_tmp)
# Easily generalizable. It could be inputted a list with the
# desired derived parameters and store the whole dictionary.
except Exception as e:
sys.stderr.write(str(self._params) + '\n')
sys.stderr.write(str(e))
sys.stderr.write('\n')
continue
if len(wzbins) == 5:
self._save_computed(params, shoot, [wzbins, wabins])
params = []
wzbins = []
wabins = []
shoot = []
self._save_computed(params, shoot, [wzbins, wabins])
def compute_Pade_from_params(self, params_func, number_of_rows):
"""
Compute the w_i bins for the models given by the function
params_func iterated #iterations.
"""
self._create_output_files()
wbins = []
params = []
shoot = []
for row in range(number_of_rows):
sys.stdout.write("{}/{}\n".format(row + 1, number_of_rows))
params_tmp = params_func().copy()
try:
wbins_tmp, shoot_tmp = self.compute_Pade_coefficients(
params_tmp)
wbins.append(wbins_tmp)
params.append(params_tmp)
shoot.append(shoot_tmp)
# Easily generalizable. It could be inputted a list with the
# desired derived parameters and store the whole dictionary.
except Exception as e:
sys.stderr.write(str(self._params) + '\n')
sys.stderr.write(str(e))
sys.stderr.write('\n')
continue
if len(wbins) == 5:
self._save_computed(params, shoot, wbins)
params = []
wbins = []
shoot = []
self._save_computed(params, shoot, wbins)
def compute_fit_from_params(self, params_func, number_of_rows):
"""
Compute the fit for the models given by the function
params_func iterated #iterations.
The variable to fit is chosen in self.set_fit
"""
# TODO: If this grows, consider creating a separate method
if self._variable_to_fit == 'F':
fit_variable_function = self.compute_fit_coefficients_for_F
elif self._variable_to_fit == 'w':
fit_variable_function = self.compute_fit_coefficients_for_w
elif self._variable_to_fit == 'logRho':
fit_variable_function = self.compute_fit_coefficients_for_logRho
elif self._variable_to_fit == 'logX':
fit_variable_function = self.compute_fit_coefficients_for_logX
elif self._variable_to_fit == 'X':
fit_variable_function = self.compute_fit_coefficients_for_X
self._create_output_files()
coeffs = []
params = []
shoot = []
for row in range(number_of_rows):
sys.stdout.write("{}/{}\n".format(row + 1, number_of_rows))
# params_tmp = params_func().copy()
try:
coeffs_tmp, shoot_tmp = fit_variable_function(params_func())
coeffs.append(coeffs_tmp)
params.append(self._params.copy())
shoot.append(shoot_tmp)
# Easily generalizable. It could be inputted a list with the
# desired derived parameters and store the whole dictionary.
except Exception as e:
sys.stderr.write(str(self._params) + '\n')
sys.stderr.write(str(e))
sys.stderr.write('\n')
continue
if len(coeffs) == 5:
self._save_computed(params, shoot, coeffs)
params = []
coeffs = []
shoot = []
self._save_computed(params, shoot, coeffs)
def compute_bins_from_file(self, path):
"""
Compute the w_i bins for the models given in path.
"""
if self._computed is True:
print(
"Bins already computed. Use reset if you want to compute it again"
)
return
self._path = path
self._read_from_file(path)
def params_gen(length):
row = 0
while row < length:
yield self._params_from_row(row)
row += 1
params = params_gen(len(self.params_smg))
self.compute_bins_from_params(params.next, len(self.params_smg))
def _create_output_files(self):
"""
Initialize the output files.
"""
# TODO: Add check if files exist
with open(self._fparamsname, 'a') as f:
f.write('# ' + "Dictionary of params to use with cosmo.set()" +
'\n')
with open(self._fshootname, 'a') as f:
f.write('# ' + "Shooting variable value" + '\n')
if self._binType == 'bins':
with open(self._fwzname, 'a') as f:
f.write('# ' + "Bins on redshift" + '\n')
f.write('# ' + str(self._zbins).strip('[]').replace('\n', '') +
'\n')
with open(self._fwaname, 'a') as f:
f.write('# ' + "Bins on scale factor" + '\n')
f.write('# ' + str(self._abins).strip('[]').replace('\n', '') +
'\n')
elif self._binType == 'Pade':
with open(self._fPadename, 'a') as f:
f.write('# ' + "Pade fit for temporal variable {} \n".format(
self._Pade_xvar))
coeff_header_num = [
'num_{}'.format(n) for n in range(self._PadeOrder[0] + 1)
]
coeff_header_den = [
'den_{}'.format(n + 1) for n in range(self._PadeOrder[1])
]
res_header = ['min(residual)', 'max(residual)']
f.write('# ' + ' '.join(coeff_header_num + coeff_header_den +
res_header) + '\n')
elif self._binType == 'fit':
with open(self._fFitname, 'a') as f:
f.write('# ' +
"{} fit for temporal variable {} of {}\n".format(
self._fit_function_label, self._fit_xvar,
self._variable_to_fit))
coeff_header_num = [
'c_{}'.format(n) for n in range(self._n_coeffs)
]
res_header = ['max(rel.dev. D_A)', 'max(rel.dev. f)']
f.write('# ' + ' '.join(coeff_header_num + res_header) + '\n')
def _save_computed(self, params, shoot, wbins):
"""
Save stored iterations in file.
"""
with open(self._fparamsname, 'a') as f:
for i in params:
f.write(str(i) + '\n')
with open(self._fshootname, 'a') as f:
np.savetxt(f, shoot)
if self._binType == 'bins':
wzbins, wabins = wbins
with open(self._fwzname, 'a') as f:
np.savetxt(f, wzbins)
with open(self._fwaname, 'a') as f:
np.savetxt(f, wabins)
elif self._binType == 'Pade':
with open(self._fPadename, 'a') as f:
np.savetxt(f, wbins)
elif self._binType == 'fit':
with open(self._fFitname, 'a') as f:
np.savetxt(f, wbins)
def reset(self):
"""
Reset class
"""
self._cosmo.struct_cleanup()
self._cosmo.empty()
self._set_default_values()
#Einstein-de Sitter
CDM = Class()
CDM.set({'Omega_cdm': 0.95, 'Omega_b': 0.05})
CDM.compute()
# Just to cross-check that Omega_Lambda is negligible
# (but not exactly zero because we neglected radiation)
derived = CDM.get_current_derived_parameters(['Omega0_lambda'])
print derived
print "Omega_Lambda =", derived['Omega0_lambda']
# In[ ]:
#Get background quantities and recover their names:
baLCDM = LCDM.get_background()
baCDM = CDM.get_background()
baCDM.viewkeys()
# In[ ]:
#Get H_0 in order to plot the distances in this unit
fLCDM = LCDM.Hubble(0)
fCDM = CDM.Hubble(0)
# In[ ]:
namelist = ['lum. dist.', 'comov. dist.', 'ang.diam.dist.']
colours = ['b', 'g', 'r']
for name in namelist:
idx = namelist.index(name)
#Einstein-de Sitter
CDM = Class()
CDM.set({'Omega_cdm':0.95,'Omega_b':0.05})
CDM.compute()
# Just to cross-check that Omega_Lambda is negligible
# (but not exactly zero because we neglected radiation)
derived = CDM.get_current_derived_parameters(['Omega0_lambda'])
print derived
print "Omega_Lambda =",derived['Omega0_lambda']
# In[ ]:
#Get background quantities and recover their names:
baLCDM = LCDM.get_background()
baCDM = CDM.get_background()
baCDM.viewkeys()
# In[ ]:
#Get H_0 in order to plot the distances in this unit
fLCDM = LCDM.Hubble(0)
fCDM = CDM.Hubble(0)
# In[ ]:
namelist = ['lum. dist.','comov. dist.','ang.diam.dist.']
colours = ['b','g','r']
lcdm = Class()
lcdm.set({
'h': df["H0*"].values[i]/100.0,
'omega_b': df.omegabh2.values[i],
'omega_cdm': df.omegach2.values[i],
'Omega_Lambda': 0.,
'w0_fld': -1.0,
'wa_fld': 0.0,
'background_verbose':1,
'input_verbose':1,
'gauge':'Newtonian',
})
lcdm.compute()
b = lcdm.get_background()
x = b["comov. dist"]
z = b["xz"]
Chi = int.interpd1(z,x)
Z_s = 0.8
Z = 0.4
Chi_s = Chi(Z_s)
Chi_z = Chi(Z)
Omega_m = np.mean(df["omegach2"].values)
H0 = np.mean(df["H0"].values)
sigma8 = np.mean(df["sigma8*"].values)
for x in range(10000):
a[i] = random.uniform(0.5,1.6,size=5)
#Perform rescaling if necessary
D_0 = cosmo.scale_independent_growth_factor(0)
D_f = cosmo.scale_independent_growth_factor(z_f)
D_lin = cosmo.scale_independent_growth_factor(z_lin)
print("D_0: ", D_0)
print("D_f: ", D_f)
print("D_lin: ", D_lin)
#MeshPT needs a z=0 power spectrum, so one can rescale a power spectrum to z=0
#if a different input redshift is desired
if (not z_lin == 0):
print("Rescaling the linear theory power spectrum")
Pvec *= (D_0 / D_lin)**2
#Get background quantities from CLASS (reverse the order of the arrays)
background = cosmo.get_background()
bg_t = background['proper time [Gyr]'][::-1]
bg_H = background['H [1/Mpc]'][::-1]
bg_D = background['gr.fac. D'][::-1]
bg_f = background['gr.fac. f'][::-1]
bg_z = background['z'][::-1]
bg_a = 1. / (1 + bg_z)
bg_rho = background['(.)rho_tot'][::-1]
bg_p = background['(.)p_tot'][::-1]
#Size of arrays of background quantities
nz = 1000
zmax = max(1000, z_f * 1.05, z_i * 1.05)
zmin = 0
#Curvature parameter
def constraints(params, zs):
cosmo = Class()
cosmo.set(params)
cosmo.compute()
h = cosmo.Hubble(0) * 299792.458
om0 = cosmo.Omega0_m()
#print(cosmo.pars)
zarr2 = cosmo.get_background().get('z')
hz = cosmo.get_background().get('H [1/Mpc]')
hf = interpolate.InterpolatedUnivariateSpline(zarr2[-1:0:-1], hz[-1:0:-1])
chiz = cosmo.get_background().get('comov. dist.')
chif = interpolate.InterpolatedUnivariateSpline(zarr2[-1:0:-1],
chiz[-1:0:-1])
pkz = np.zeros((nk, nz))
pklz2 = np.zeros((nk, nz))
dprime = np.zeros((nk, 1))
zarr = np.linspace(0, zmax, nz)
karr = np.logspace(-3, np.log10(20), nk)
rcollarr = np.zeros(nz)
kcarr = np.zeros(nz)
delz = 0.01
for i in np.arange(nz):
Dz = (cosmo.scale_independent_growth_factor(zarr[i]) /
cosmo.scale_independent_growth_factor(0))
sigz = lambda x: Dz * cosmo.sigma(x, zarr[i]) - 1.192182033080519
if (sigz(1e-5) > 0) & (sigz(10) < 0):
rcollarr[i] = optimize.brentq(sigz, 1e-5, 10)
else:
rcollarr[i] = 0
for j in np.arange(nk):
pkz[j, i] = cosmo.pk(karr[j], zarr[i])
for i in np.arange(nk):
pklz0 = np.log(cosmo.pk(karr[i], zs - delz) / cosmo.pk(karr[i], 0))
pklz1 = np.log(cosmo.pk(karr[i], zs + delz) / cosmo.pk(karr[i], 0))
pklz2[i] = cosmo.pk(karr[i], 0)
dprime[i] = -hf(zs) * np.sqrt(
cosmo.pk(karr[i], zs) / pklz2[i, 0]) * (pklz1 - pklz0) / 4 / delz
#divided by 2 for step size, another for defining D'
w0 = params.get('w0_fld')
wa = params.get('wa_fld')
mt = 5 * np.log10(cosmo.luminosity_distance(zs))
#mt = 5*np.log10(fanal.dlatz(zs, om0, og0, w0, wa))
Rc = (2 * 4.302e-9 * Mc / h**2 / om0)**(1 / 3)
mask = (0 < rcollarr) & (rcollarr < Rc)
kcarr[mask] = 2 * np.pi / rcollarr[mask]
mask = (rcollarr >= Rc)
kcarr[mask] = 2 * np.pi / Rc
#plt.semilogy(zarr, kcarr)
pksmooth = pdf.pkint(karr, zarr, pkz, kcarr)
par2 = {'w0': w0, 'wa': wa, 'Omega_m': om0}
print(par2)
#kmin = conv.kmin(zs, chif, hf)*(-3./2.*hf(0)**2*om0)
kvar = conv.kvar(zs, pksmooth, chif, hf) * (3. / 2. * hf(0)**2 * om0)**2
#sigln = np.log(1+kvar/np.abs(kmin)**2)
#L = pdf.convpdf(kmin, sigln, sig, mfid-mt)
#sigln = np.sqrt(sig**2+(5/np.log(10))**2*kvar)
#L = pdf.gausspdf(sig, mfid-mt)
#lnL = mt/sig
vvar = np.trapz(pklz2[:, 0] * dprime[:, 0]**2,
karr) / 6 / np.pi**2 * (1 -
(1 + zs) / hf(zs) / chif(zs))**2
#var_tot = norm*kvar+sig**2
lnL = -mt**2 / 2
print('Sigmasq = {}, {}, Likelihood = {}'.format(kvar, vvar, lnL))
cosmo.struct_cleanup()
cosmo.empty()
return [mt, kvar, vvar]
#[mt, var_tot]
class MultiCorr(object):
"""
This class computes the relativistic 2PCF and power-spectra multipoles
!!! -> Fiducial cosmology must be directly altered in this file! <- !!!
This makes the code more flexible, so you can compute the
multipoles at any redshift and with all the parameters you
wish.
OBS: non-Gaussian corrections have not been implemented here yet.
Once it is functional, this class will depend on halo tools.
No variables are needed to initialize the class
"""
init = False
def __init__(self):
print '[Initializing MultiCorr]: Relativistic 2PCF and power-spectra multipoles\n'
# Fiducial cosmology
c = 299792.458
self.c = c
h = 0.67556
self.h = h
n_s = 0.9619
self.n_s = n_s
A_s = 2.215e-9
self.A_s = A_s
omega_cdm = 0.12038
self.omega_cdm = omega_cdm
omega_b = 0.022032
self.omega_b = omega_b
omega_k = 0.0
self.omega_k = omega_k
k_pivot = 0.05
self.k_pivot = k_pivot
N_ur = 3.046
self.N_ur = N_ur
N_ncdm = 0.0
self.N_ncdm = N_ncdm
T_cmb = 2.7255
self.T_cmb = T_cmb
Omega_m = (omega_cdm + omega_b) / h**2
self.Omega_m = Omega_m
w = -1.0
self.w = w
print '(Pre-defined cosmology)'
print 'h:', h
print 'n_s:', n_s
print 'A_s:', A_s
print 'N_ur:', N_ur
print 'N_ncdm:', N_ncdm
print 'T_cmb:', T_cmb
print 'Omega_m:', Omega_m
print 'w:', w
print '\n'
self.kvec = np.logspace(-7., np.log10(500), 1000)
self.k_ = np.logspace(-3, 2, 2000)
# Linear power-spectrum
class_settings = {
'output': 'mPk',
'lensing': 'no',
'h': h,
'n_s': n_s,
'A_s': A_s,
'omega_cdm': omega_cdm,
'omega_b': omega_b,
'Omega_k': 0.0,
'k_pivot': k_pivot,
'z_max_pk': 10.,
'N_ur': N_ur,
'N_ncdm': N_ncdm,
'T_cmb': T_cmb,
'P_k_max_1/Mpc': 500
}
self.pclass = Class()
self.pclass.set(class_settings)
self.pclass.compute()
# These are the CLASS functions (has 0.5% accuracy wrt to the ones I implement by hand)
self.bg = self.pclass.get_background()
self.H = interp1d(self.bg['z'], (c / h) * self.bg['H [1/Mpc]'])
self.comov = interp1d(self.bg['z'], h * self.bg['comov. dist.'])
self.fz = interp1d(self.bg['z'], self.bg['gr.fac. f'])
# Non-linear power-spectrum
class_settings_nl = {
'output': 'mPk',
'lensing': 'no',
'h': h,
'n_s': n_s,
'A_s': A_s,
'omega_cdm': omega_cdm,
'omega_b': omega_b,
'Omega_k': 0.0,
'k_pivot': k_pivot,
'z_max_pk': 10.,
'non linear': 'halofit',
'N_ur': N_ur,
'N_ncdm': N_ncdm,
'T_cmb': T_cmb,
'P_k_max_1/Mpc': 500
}
self.pnlclass = Class()
self.pnlclass.set(class_settings_nl)
self.pnlclass.compute()
print '[MultiCorr Initialized]'
self.init = True
######################################################################################################
# Useful functions
def Om(self, z):
"""
Returns the evolving matter density Omega_m(z)
"""
self.z = z
self.H0Hz2 = pow(1 + self.z, 3.0) * self.Omega_m + (1 - self.Omega_m)
return pow(1 + self.z, 3.0) * self.Omega_m / self.H0Hz2
######################################################################################################
# RSD
def Doppler(self, z_, be_):
"""
Computes the doppler assuming the magnification bias s = 0.
"""
self.z_ = z_
self.be_ = be_
self.scale_factor = 1.0 / (1.0 + self.z_)
self.Hubble = self.H(self.z_) # (h/Mpc) (Km/s)
self.R_at_z = self.comov(self.z_) # (Mpc/h)
return self.be_ - (1.0 - 1.5 * self.Om(self.z_)) - (
2.0 / (self.scale_factor * (self.Hubble / self.c) * self.R_at_z))
def DopplerMag(self, z_, be_, sm_):
"""
Computes the doppler assuming a non-vanishing magnification bias s != 0.
"""
self.z_ = z_
self.be_ = be_
self.sm_ = sm_
self.scale_factor = 1.0 / (1.0 + self.z_)
self.Hubble = self.H(self.z_) # (h/Mpc) (Km/s)
self.R_at_z = self.comov(self.z_) # (Mpc/h)
return self.be_ - 5 * self.sm_ - (1.0 - 1.5 * self.Om(self.z_)) - (
1.0 /
(self.scale_factor *
(self.Hubble / self.c) * self.R_at_z)) * (2.0 - 5 * self.sm_)
def c0(self, z_, b_alpha_, b_beta_):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
return self.b_alpha_ * self.b_beta_ + (1.0 / 3.0) * self.fz(
self.z_) * (self.b_alpha_ + self.b_beta_) + (1.0 / 5.0) * pow(
self.fz(self.z_), 2.0)
def c1(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_):
self.z_ = z_
self.scale_factor = 1.0 / (1.0 + self.z_)
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.Hubble = self.H(self.z_) # (h/Mpc) (Km/s)
self.term1 = self.Doppler(self.z_, self.be_alpha_) * (
3 * self.fz(self.z_) + 5 * self.b_beta_)
self.term2 = self.Doppler(self.z_, self.be_beta_) * (
3 * self.fz(self.z_) + 5 * self.b_alpha_)
return (self.fz(self.z_) / 5.0) * (self.Hubble /
(self.c * self.k_)) * (self.term1 -
self.term2)
def c1_Mag(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_, s_alpha,
s_beta):
self.z_ = z_
self.scale_factor = 1.0 / (1.0 + self.z_)
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.s_alpha = s_alpha
self.s_beta = s_beta
self.Hubble = self.H(self.z_) # (h/Mpc) (Km/s)
self.term1 = self.DopplerMag(self.z_, self.be_alpha_, self.s_alpha) * (
3 * self.fz(self.z_) + 5 * self.b_beta_)
self.term2 = self.DopplerMag(self.z_, self.be_beta_, self.s_beta) * (
3 * self.fz(self.z_) + 5 * self.b_alpha_)
return (self.fz(self.z_) / 5.0) * (self.scale_factor * self.Hubble /
(self.c * self.k_)) * (self.term1 -
self.term2)
def c2(self, z_, b_alpha_, b_beta_):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
return (2.0 / 3.0) * self.fz(
self.z_) * (self.b_alpha_ + self.b_beta_) + (4.0 / 7.0) * pow(
self.fz(self.z_), 2.0)
def c3(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_):
self.z_ = z_
self.scale_factor = 1.0 / (1.0 + self.z_)
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.Hubble = self.H(self.z_) # (h/Mpc) (Km/s)
self.term1 = self.Doppler(self.z_, self.be_alpha_)
self.term2 = self.Doppler(self.z_, self.be_beta_)
return (2.0 / 5.0) * pow(self.fz(
self.z_), 2.0) * (self.scale_factor * self.Hubble /
(self.c * self.k_)) * (self.term1 - self.term2)
def c4(self, z_):
self.z_ = z_
return (8.0 / 35.0) * pow(self.fz(self.z_), 2.0)
######################################################################################################
# Plane-parallel power-spectrum
## First order in $\mathcal{H}/k$
def P0_pp(self, z_, b_alpha_, b_beta_, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c0(self.z_, self.b_alpha_, self.b_beta_) * self.Pk(k_)
def P1_pp(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c1(self.z_, self.b_alpha_, self.b_beta_, self.be_alpha_,
self.be_beta_) * self.Pk(self.k_)
def P1Mag_pp(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_, s_alpha,
s_beta, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.s_alpha = s_alpha
self.s_beta = s_beta
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c1_Mag(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_, self.s_alpha,
self.s_beta) * self.Pk(self.k_)
def P2_pp(self, z_, b_alpha_, b_beta_, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c2(self.z_, self.b_alpha_, self.b_beta_) * self.Pk(self.k_)
def P3_pp(self, z_, b_alpha_, b_beta_, be_alpha_, be_beta_, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c3(self.k_, self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_) * self.Pk(self.k_)
def P4_pp(self, z_, b_alpha_, b_beta_, linear):
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.linear = linear
if linear == 'linear' or linear == True:
self.Pk = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.Pk = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
return self.c4(self.z_) * self.Pk(self.k_)
#################################################################################################################################
# Correlation function multipoles
import scipy
def integrand(self, s, ell):
self.s = s
self.ell = ell
return (pow(self.k_, 2.) * scipy.special.spherical_jn(
self.ell, self.k_ * self.s)) / (2 * pow(np.pi, 2.))
def matter_real_space(self, s, z_, args):
self.s = s
self.z_ = z_
self.args = args
self.linear = self.args[0]
if self.linear == 'linear' or self.linear == True:
self.interpol = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.interpol = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
self.MONO = []
for i in s:
self.MONO.append(
integrate.simps(
self.integrand(i, 0) * self.interpol(self.k_) *
np.exp(-self.k_**5), self.k_))
self.MONO = np.array(self.MONO)
return self.MONO
def multipoles(self, s, z_, b_alpha_, b_beta_, be_alpha_, be_beta_, args):
self.s = s
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.args = args
"""
This gives the non-vanishing ODD multipoles (l=1,3) for the correlation function.
Inputs are:
1) s: a vector of configuration space positions;
2) z: redshift of calculation;
3) b_alpha, b_beta: linear biases of tracers alpha and beta (may be the same);
4) be_alpha, be_beta: evolution biases for the tracers computed at z;
5) args: a list or array with like
args = ['linear',s_alpha,s_beta] (s = magnification biases)
or args = ['linear']
or True/False to select linear or non-linear correlation functions;
"""
if len(self.args) > 1:
self.linear, self.s_alpha, self.s_beta = self.args[0], self.args[
1], self.args[2]
self.coef_dipo = self.c1_Mag(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_,
self.s_alpha, self.s_beta)
if len(self.args) == 1:
self.linear = self.args[0]
self.coef_dipo = self.c1(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_)
self.coef_mono = self.c0(self.z_, self.b_alpha_, self.b_beta_)
self.coef_quad = self.c2(self.z_, self.b_alpha_, self.b_beta_)
self.coef_octu = self.c3(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_)
self.coef_hexa = self.c4(self.z_)
if self.linear == 'linear' or self.linear == True:
self.interpol = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.interpol = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
self.MONO = []
self.DIPO = []
self.QUAD = []
self.OCTU = []
self.HEXA = []
for i in s:
self.MONO.append(
integrate.simps(
self.coef_mono * self.integrand(i, 0) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.DIPO.append(
integrate.simps(
self.coef_dipo * self.integrand(i, 1) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.QUAD.append(
integrate.simps(
self.coef_quad * self.integrand(i, 2) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.OCTU.append(
integrate.simps(
self.coef_octu * self.integrand(i, 3) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.HEXA.append(
integrate.simps(
self.coef_hexa * self.integrand(i, 4) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.MONO = pow(1j, 0).real * np.array(self.MONO)
self.DIPO = pow(1j, 1).imag * np.array(self.DIPO)
self.QUAD = pow(1j, 2).real * np.array(self.QUAD)
self.OCTU = pow(1j, 3).imag * np.array(self.OCTU)
self.HEXA = pow(1j, 4).real * np.array(self.HEXA)
return self.MONO, self.DIPO, self.QUAD, self.OCTU, self.HEXA
def odd_multipoles(self, s, z_, b_alpha_, b_beta_, be_alpha_, be_beta_,
args):
self.s = s
self.z_ = z_
self.b_alpha_ = b_alpha_
self.b_beta_ = b_beta_
self.be_alpha_ = be_alpha_
self.be_beta_ = be_beta_
self.args = args
"""
This gives the non-vanishing ODD multipoles (l=1,3) for the correlation function.
Inputs are:
1) s: a vector of configuration space positions;
2) z: redshift of calculation;
3) b_alpha, b_beta: linear biases of tracers alpha and beta (may be the same);
4) be_alpha, be_beta: evolution biases for the tracers computed at z;
5) args: a list or array with like
args = ['linear',s_alpha,s_beta] (s = magnification biases)
or args = ['linear']
or True/False to select linear or non-linear correlation functions;
"""
if len(self.args) > 1:
self.linear, self.s_alpha, self.s_beta = self.args[0], self.args[
1], self.args[2]
self.coef_dipo = self.c1_Mag(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_,
self.s_alpha, self.s_beta)
if len(self.args) == 1:
self.linear = self.args[0]
self.coef_dipo = self.c1(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_)
self.coef_octu = self.c3(self.z_, self.b_alpha_, self.b_beta_,
self.be_alpha_, self.be_beta_)
if self.linear == 'linear' or self.linear == True:
self.interpol = interp1d(
self.kvec / self.h,
np.array([self.pclass.pk(_k, self.z_)
for _k in self.kvec]) * (self.h**3.))
else:
self.interpol = interp1d(
np.logspace(-5, 2, 1000) / self.h,
np.array([
self.pnlclass.pk(_k, self.z_)
for _k in np.logspace(-5, 2, 1000)
]) * (self.h**3.))
self.DIPO = []
self.OCTU = []
for i in s:
self.DIPO.append(
integrate.simps(
self.coef_dipo * self.integrand(i, 1) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.OCTU.append(
integrate.simps(
self.coef_octu * self.integrand(i, 3) *
self.interpol(self.k_) * np.exp(-self.k_**5), self.k_))
self.DIPO = pow(1j, 1).imag * np.array(self.DIPO)
self.OCTU = pow(1j, 3).imag * np.array(self.OCTU)
return self.DIPO, self.OCTU
def help(self):
print '[Reaching help for MultiCorr]\n'
print 'Full list of functions and their entries:\n'
print '(Auxiliary functions)'
print 'Om(z): Evolving matter density Omega_m(z) - Inputs: z (redshift).'
print 'Doppler(z, be): Doppler term assuming magnification bias s = 0 - Inputs: z (redshift) and be (evolution bias at z).'
print 'DopplerMag(z, be,s): Doppler term - Inputs: z (redshift), be (evolution bias at z) and s (magnification bias at z).'
print 'c0(z,ba,bb): Monopole coefficient - Inputs: z (redshift) and bi (linear bias of tracer i at z).'
print 'c1(z,ba,bb,be_a,be_b): Dipole coefficient - Inputs: z (redshift), bi (linear bias of tracer i at z) and be_i (evolution bias of tracer i at z).'
print 'c1_Mag(z,ba,bb,be_a,be_b,sa,sb): Dipole coefficient with magnification - Inputs: z (redshift), bi (linear bias of tracer i at z), be_i (evolution bias of tracer i at z) and si (magnification of tracer i at z).'
print 'c2(z,ba,bb): Quadrupole coefficient - Inputs: z (redshift) and bi (linear bias of tracer i at z).'
print 'c3(z,ba,bb,be_a,be_b): Octupole coefficient assuming no magnification - Inputs: z (redshift), bi (linear bias of tracer i at z) and be_i (evolution bias of tracer i at z).'
print 'c4(z): Hexadecapole coefficient - Inputs: z (redshift).\n'
print '(Power-spectrum multipoles)'
print 'P0_pp(z,ba,bb,linear) - linear = True or False'
print 'P1_pp(z,ba,bb,be_a,be_b,linear) - linear = True or False'
print 'P1Mag_pp(z,ba,bb,be_a,be_b,sa,sb,linear) - linear = True or False'
print 'P2_pp(z,ba,bb,linear) - linear = True or False'
print 'P3_pp(z,ba,bb,be_a,be_b,linear) - linear = True or False'
print 'P4_pp(z,ba,bb,linear) - linear = True or False\n'
print '(Correlation function multipoles)'
print 'integrand(s,ell): returns the integrand of the correlation function.'
print 'multipoles(s,z,ba,bb,be_a,be_b,args) - args: [True] or [True, sa, sb], si (magnification bias of tracer i)'
print 'odd_multipoles(s,z,ba,bb,be_a,be_b,args) - args: [True] or [True, sa, sb], si (magnification bias of tracer i)\n'
print '[End of help]'
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()
z_c = M.z_c()
baH = baM['H [1/Mpc]']
baT = baM['conf. time [Mpc]']
baa = 1/(1 + baM['z'])
bV = baM['V_e_scf']
bpp = baM["phi'_scf"]
baCrit = baM['(.)rho_crit']
rho_scf = (bpp*bpp/(2*baa*baa) + bV)/3.
plt.figure(figsize=(10,10))
plt.rcParams.update({'font.size': 18})