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classWrapTools.py
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import os
import numpy as np
import cambWrapTools
TCMB = 2.7255 ## CMB temperature in K
def class_generate_data(cosmo,
rootName = 'testing',
cmbNoise = None,
noiseLevel = 1.,
beamSizeArcmin = 1.,
deflectionNoise = None,
externalUnlensedCMBSpectra = None,
externalLensedCMBSpectra = None,
externalLensingSpectra = None,
classExecDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
calculateDerivatives = False,
includeUnlensedSpectraDerivatives = False,
outputAllReconstructions = False,
reconstructionMask = None,
lmax = 5000,
backgroundOnly = False,
extraParams = dict()
):
if not os.path.exists(classDataDir + 'input/'):
os.makedirs(classDataDir + 'input/')
if not os.path.exists(classDataDir + 'output/'):
os.makedirs(classDataDir + 'output/')
########################################################
## Convert cosmological parameters to CLASS format ##
########################################################
cosmoclass = dict()
if 'H0' in list(cosmo.keys()):
cosmoclass['H0'] = cosmo['H0']
elif 'h' in list(cosmo.keys()):
cosmoclass['h'] = cosmo['h']
elif 'theta_s' in list(cosmo.keys()):
cosmoclass['100*theta_s']= 100.*cosmo['theta_s']
cosmoclass['omega_b'] = cosmo['omega_b_h2']
cosmoclass['omega_cdm'] = cosmo['omega_c_h2']
if 'A_s' in list(cosmo.keys()):
cosmoclass['A_s'] = cosmo['A_s']
elif 'logA' in list(cosmo.keys()):
cosmoclass['ln10^{10}A_s'] = cosmo['logA']
cosmoclass['n_s'] = cosmo['n_s']
cosmoclass['tau_reio'] = cosmo['tau']
#omega_k
if 'omk' in list(cosmo.keys()):
cosmoclass['Omega_k'] = cosmo['omk']
## CLASS treats 'ur' species differently from 'ncdm' species
## With one massive neutrino eigenstate, 'N_ur' is 2.0328 to make N_eff 3.046
## Subtract off the difference to give consistent N_eff
cosmoclass['N_ur'] = cosmo['N_eff']-(3.046 - 2.0328) if 'mnu' in list(cosmo.keys()) else cosmo['N_eff'] if 'N_eff' in list(cosmo.keys()) else 3.046
## Setup massive neutrinos with a single massive species
if 'mnu' in list(cosmo.keys()):
cosmoclass['N_ncdm'] = 1
cosmoclass['T_ncdm'] = 0.71611
cosmoclass['m_ncdm'] = cosmo['mnu']
if 'Yhe' in list(cosmo.keys()):
cosmoclass['YHe'] = cosmo['Yhe']
if 'r' in list(cosmo.keys()) and cosmo['r'] != 0.:
cosmoclass['r'] = cosmo['r']
if 'n_t' in list(cosmo.keys()):
cosmoclass['n_t'] = cosmo['n_t']
## spectral running
if 'alpha_s' in list(cosmo.keys()):
cosmoclass['alpha_s'] = cosmo['alpha_s']
## isocurvature parameters
if 'f_bi' in list(cosmo.keys()):
cosmoclass['f_bi'] = cosmo['f_bi']
if 'n_bi' in list(cosmo.keys()):
cosmoclass['n_bi'] = cosmo['n_bi']
if 'alpha_bi' in list(cosmo.keys()):
cosmoclass['alpha_bi'] = cosmo['alpha_bi']
if 'f_cdi' in list(cosmo.keys()):
cosmoclass['f_cdi'] = cosmo['f_cdi']
if 'n_cdi' in list(cosmo.keys()):
cosmoclass['n_cdi'] = cosmo['n_cdi']
if 'alpha_cdi' in list(cosmo.keys()):
cosmoclass['alpha_cdi'] = cosmo['alpha_cdi']
if 'f_nid' in list(cosmo.keys()):
cosmoclass['f_nid'] = cosmo['f_nid']
if 'n_nid' in list(cosmo.keys()):
cosmoclass['n_nid'] = cosmo['n_nid']
if 'alpha_nid' in list(cosmo.keys()):
cosmoclass['alpha_nid'] = cosmo['alpha_nid']
if 'f_niv' in list(cosmo.keys()):
cosmoclass['f_niv'] = cosmo['f_niv']
if 'n_niv' in list(cosmo.keys()):
cosmoclass['n_niv'] = cosmo['n_niv']
if 'alpha_niv' in list(cosmo.keys()):
cosmoclass['alpha_niv'] = cosmo['alpha_niv']
## isocurvature covariances
if 'c_ad_bi' in list(cosmo.keys()):
cosmoclass['c_ad_bi'] = cosmo['c_ad_bi']
if 'n_ad_bi' in list(cosmo.keys()):
cosmoclass['n_ad_bi'] = cosmo['n_ad_bi']
if 'alpha_ad_bi' in list(cosmo.keys()):
cosmoclass['alpha_ad_bi'] = cosmo['alpha_ad_bi']
if 'c_ad_cdi' in list(cosmo.keys()):
cosmoclass['c_ad_cdi'] = cosmo['c_ad_cdi']
if 'n_ad_cdi' in list(cosmo.keys()):
cosmoclass['n_ad_cdi'] = cosmo['n_ad_cdi']
if 'alpha_ad_cdi' in list(cosmo.keys()):
cosmoclass['alpha_ad_cdi'] = cosmo['alpha_ad_cdi']
if 'c_ad_nid' in list(cosmo.keys()):
cosmoclass['c_ad_nid'] = cosmo['c_ad_nid']
if 'n_ad_nid' in list(cosmo.keys()):
cosmoclass['n_ad_nid'] = cosmo['n_ad_nid']
if 'alpha_ad_nid' in list(cosmo.keys()):
cosmoclass['alpha_ad_nid'] = cosmo['alpha_ad_nid']
if 'c_ad_niv' in list(cosmo.keys()):
cosmoclass['c_ad_niv'] = cosmo['c_ad_niv']
if 'n_ad_niv' in list(cosmo.keys()):
cosmoclass['n_ad_niv'] = cosmo['n_ad_niv']
if 'alpha_ad_niv' in list(cosmo.keys()):
cosmoclass['alpha_ad_niv'] = cosmo['alpha_ad_niv']
if 'c_bi_cdi' in list(cosmo.keys()):
cosmoclass['c_bi_cdi'] = cosmo['c_bi_cdi']
if 'n_bi_cdi' in list(cosmo.keys()):
cosmoclass['n_bi_cdi'] = cosmo['n_bi_cdi']
if 'alpha_bi_cdi' in list(cosmo.keys()):
cosmoclass['alpha_bi_cdi'] = cosmo['alpha_bi_cdi']
if 'c_bi_nid' in list(cosmo.keys()):
cosmoclass['c_bi_nid'] = cosmo['c_bi_nid']
if 'n_bi_nid' in list(cosmo.keys()):
cosmoclass['n_bi_nid'] = cosmo['n_bi_nid']
if 'alpha_bi_nid' in list(cosmo.keys()):
cosmoclass['alpha_bi_nid'] = cosmo['alpha_bi_nid']
if 'c_bi_niv' in list(cosmo.keys()):
cosmoclass['c_bi_niv'] = cosmo['c_bi_niv']
if 'n_bi_niv' in list(cosmo.keys()):
cosmoclass['n_bi_niv'] = cosmo['n_bi_niv']
if 'alpha_bi_nid' in list(cosmo.keys()):
cosmoclass['alpha_bi_niv'] = cosmo['alpha_bi_niv']
if 'c_cdi_nid' in list(cosmo.keys()):
cosmoclass['c_cdi_nid'] = cosmo['c_cdi_nid']
if 'n_cdi_nid' in list(cosmo.keys()):
cosmoclass['n_cdi_nid'] = cosmo['n_cdi_nid']
if 'alpha_cdi_nid' in list(cosmo.keys()):
cosmoclass['alpha_cdi_nid'] = cosmo['alpha_cdi_nid']
if 'c_cdi_niv' in list(cosmo.keys()):
cosmoclass['c_cdi_niv'] = cosmo['c_cdi_niv']
if 'n_cdi_niv' in list(cosmo.keys()):
cosmoclass['n_cdi_niv'] = cosmo['n_cdi_niv']
if 'alpha_cdi_niv' in list(cosmo.keys()):
cosmoclass['alpha_cdi_niv'] = cosmo['alpha_cdi_niv']
if 'c_nid_niv' in list(cosmo.keys()):
cosmoclass['c_nid_niv'] = cosmo['c_nid_niv']
if 'n_nid_niv' in list(cosmo.keys()):
cosmoclass['n_nid_niv'] = cosmo['n_nid_niv']
if 'alpha_nid_niv' in list(cosmo.keys()):
cosmoclass['alpha_nid_niv'] = cosmo['alpha_nid_niv']
## varying fundamental constants
if 'varying_transition_redshift' in list(cosmo.keys()):
cosmoclass['varying_fundamental_constants'] = 'instantaneous'
cosmoclass['varying_transition_redshift'] = cosmo['varying_transition_redshift']
if 'varying_alpha' in list(cosmo.keys()):
cosmoclass['varying_fundamental_constants'] = 'instantaneous'
cosmoclass['varying_alpha'] = cosmo['varying_alpha']
if 'varying_me' in list(cosmo.keys()):
cosmoclass['varying_fundamental_constants'] = 'instantaneous'
cosmoclass['varying_me'] = cosmo['varying_me']
if 'bbn_alpha_sensitivity' in list(cosmo.keys()):
cosmoclass['varying_fundamental_constants'] = 'instantaneous'
cosmoclass['bbn_alpha_sensitivity'] = cosmo['bbn_alpha_sensitivity']
## EDE-edit
if 'f_scf' in list(cosmo.keys()):
cosmoclass['f_scf'] = cosmo['f_scf']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'log10f_scf' in list(cosmo.keys()):
cosmoclass['log10f_scf'] = cosmo['log10f_scf']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'm_scf' in list(cosmo.keys()):
cosmoclass['m_scf'] = cosmo['m_scf']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'log10m_scf' in list(cosmo.keys()):
cosmoclass['log10m_scf'] = cosmo['log10m_scf']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'fEDE' in list(cosmo.keys()):
cosmoclass['fEDE'] = cosmo['fEDE']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'log10z_c' in list(cosmo.keys()):
cosmoclass['log10z_c'] = cosmo['log10z_c']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
if 'thetai_scf' in list(cosmo.keys()):
cosmoclass['thetai_scf'] = cosmo['thetai_scf']
cosmoclass['Omega_Lambda'] = 0
cosmoclass['Omega_fld'] = 0
cosmoclass['Omega_scf'] = -1
cosmoclass['scf_parameters'] = '1, 1, 1, 1, 1, 0.0'
cosmoclass['CC_scf'] = 1
cosmoclass['n_scf'] = 3
cosmoclass['scf_tuning_index'] = 3
cosmoclass['attractor_ic_scf'] = 'no'
## Dark radiation (7.2.2)
if 'N_idr' in list(cosmo.keys()):
cosmoclass['N_idr'] = cosmo['N_idr']
if 'f_idm' in list(cosmo.keys()):
cosmoclass['f_idm'] = cosmo['f_idm']
if 'Gamma_0_nadm' in list(cosmo.keys()):
cosmoclass['Gamma_0_nadm'] = cosmo['Gamma_0_nadm']
########################################################
## the relevant CLASS commands to call delensing code ##
########################################################
dcode = dict()
dcode['root'] = classDataDir + 'output/' + rootName #+ '_'
dcode['output'] = 'tCl,pCl,lCl,dlCl'
dcode['modes'] = 's'
dcode['lensing'] = 'yes'
dcode['non linear'] = 'hmcode'
if dcode['non linear'] == 'hmcode' and 'eta_0' in list(cosmo.keys()):
cosmoclass['eta_0'] = cosmo['eta_0']
if dcode['non linear'] == 'hmcode' and 'c_min' in list(cosmo.keys()):
cosmoclass['c_min'] = cosmo['c_min']
dcode['l_max_scalars'] = lmax
dcode['delta_l_max'] = 2000
dcode['delta_dl_max'] = 0
dcode['format'] = 'class'
dcode['accurate_lensing'] = 1
dcode['headers'] = 'yes'
dcode['output_spectra_noise'] = 'yes'
dcode['delensing_verbose'] = 0
dcode['write parameters'] = 'yes'
dcode['delensing derivatives'] = 'no'
dcode['output_derivatives'] = 'no'
dcode['overwrite_root'] = 'yes'
dcode['delensing_verbose'] = 3
dcode['ic'] = 'ad'
if 'r' in list(cosmo.keys()) and cosmo['r'] != 0.:
dcode['modes']+=',t'
dcode['l_max_tensors'] = 1000
## adding isocurvature modes to initial conditions
if 'f_bi' in list(cosmo.keys()) and cosmo['f_bi'] != 0.:
dcode['ic']+=',bi'
if 'f_cdi' in list(cosmo.keys()) and cosmo['f_cdi'] != 0.:
dcode['ic']+=',cdi'
if 'f_nid' in list(cosmo.keys()) and cosmo['f_nid'] != 0.:
dcode['ic']+=',nid'
if 'f_niv' in list(cosmo.keys()) and cosmo['f_niv'] != 0.:
dcode['ic']+=',niv'
if cmbNoise is None:
## If T and P noise are not provided, use analytic noise model
dcode['temperature noise spectra type'] = 'idealized'
dcode['polarization noise spectra type'] = 'idealized'
dcode['delta_noise'] = noiseLevel / 60. / 180. * np.pi ## Convert uK-arcmin to uK-rad
dcode['sigma_beam'] = beamSizeArcmin / 60. / 180. * np.pi ## Convert arcmin to rad
else:
if cmbNoise['l'][-1]<dcode['l_max_scalars']+dcode['delta_l_max']+dcode['delta_dl_max']:
print('You must provde external CMB noise at least up to l=' +str(dcode['l_max_scalars']+dcode['delta_l_max']+dcode['delta_dl_max']))
stop
dcode['temperature noise spectra type'] = 'external'
dcode['polarization noise spectra type'] = 'external'
np.savetxt(classDataDir + 'input/' + rootName + '_temp_noise.dat', np.column_stack((cmbNoise['l'], cmbNoise['cl_TT'])) )
np.savetxt(classDataDir + 'input/' + rootName + '_pol_noise.dat', np.column_stack((cmbNoise['l'], cmbNoise['cl_EE'])) )
dcode['command_for_temperature_noise_spec'] = 'cat ' + classDataDir + 'input/' + rootName + '_temp_noise.dat'
dcode['command_for_polarization_noise_spec'] = 'cat ' + classDataDir + 'input/' + rootName + '_pol_noise.dat'
if deflectionNoise is None:
## If deflection noise is not provided, use iterative delensing
dcode['delensing'] = 'iterative'
dcode['lensing reconstruction noise spectra type'] = 'internal'
dcode['noise_iteration_type'] = 'diag'
dcode['min_varr_type'] = 'diag'
dcode['convergence type'] = 'total'
dcode['convergence_criterion_itr'] = '1e-5'
if reconstructionMask is not None:
dcode['recon_mask_lmin_T'] = reconstructionMask['lmin_T'] if 'lmin_T' in reconstructionMask.keys() else 0
dcode['recon_mask_lmax_T'] = reconstructionMask['lmax_T'] if 'lmax_T' in reconstructionMask.keys() else 30000
dcode['recon_mask_lmin_E'] = reconstructionMask['lmin_E'] if 'lmin_E' in reconstructionMask.keys() else 0
dcode['recon_mask_lmax_E'] = reconstructionMask['lmax_E'] if 'lmax_E' in reconstructionMask.keys() else 30000
dcode['recon_mask_lmin_B'] = reconstructionMask['lmin_B'] if 'lmin_B' in reconstructionMask.keys() else 0
dcode['recon_mask_lmax_B'] = reconstructionMask['lmax_B'] if 'lmax_B' in reconstructionMask.keys() else 30000
else:
dcode['delensing'] = 'yes'
dcode['lensing reconstruction noise spectra type'] = 'external'
## Write file containing deflection noise, assuming deflectionNoise is array of NLdd at every L starting from 2
np.savetxt(classDataDir + 'input/' + rootName + '_defl_noise.dat', np.column_stack((np.arange(len(deflectionNoise))+2, deflectionNoise)) )
dcode['command_for_lens_recon_noise_spec'] = 'cat ' + classDataDir + 'input/' + rootName + '_defl_noise.dat'
if externalUnlensedCMBSpectra is None:
## If unlensed spectra are not provided, calculate spectra with CLASS
dcode['cmb spectra type'] = 'internal'
else:
## If unlensed spectra are provided, read them in
if externalLensingSpectra is None:
print('Need to supply lensing spectrum to use external unlensed spectra')
stop
## Need unlensed spectra and lensing spectra to higher l than lmax
if dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max']) > np.shape(externalUnlensedCMBSpectra['l'])[0]:
print(('Need to supply unlensed spectrum to lmax >= ' + str( dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max'])+1 ) + \
' in order to compute delensed spectrum to lmax = ' + str( lmax ) ))
stop
if dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max']) > np.shape(externalLensingSpectra['cl_phiphi'])[0]:
print(('Need to supply lensing spectrum to lmax >= ' + str( dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max'])+1 ) + \
' in order to compute delensed spectrum to lmax = ' + str( lmax ) ))
stop
dcode['cmb spectra type'] = 'external'
np.savetxt(classDataDir + 'input/' + rootName + '_unlensed_input_spectra.dat', \
np.column_stack((externalUnlensedCMBSpectra['l'], externalUnlensedCMBSpectra['cl_TT'], \
externalUnlensedCMBSpectra['cl_TE'], externalUnlensedCMBSpectra['cl_EE'], \
externalUnlensedCMBSpectra['cl_BB'], externalLensingSpectra['cl_phiphi'] )) )
dcode['command_for_external_cmb_spectra'] = 'cat ' + classDataDir + 'input/' + rootName + '_unlensed_input_spectra.dat'
if externalLensedCMBSpectra is None:
## If lensed spectra are not provided, calculate spectra with CLASS
dcode['lensed cmb spectra type'] = 'internal'
else:
print('External lensed spectra not currently working, pass only external unlensed spectra.')
stop
## If lensed spectra are provided, read them in
if externalLensingSpectra is None:
print('Need to supply lensing spectrum to use external lensed spectra')
stop
## Need lensed spectra and lensing spectra to higher l than lmax
if dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max']) > np.shape(externalLensedCMBSpectra['l'])[0]:
print(('Need to supply lensed spectrum to lmax >= ' + str( dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max'])+1 ) + \
' in order to compute delensed spectrum to lmax = ' + str( lmax ) ))
stop
if dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max']) > np.shape(externalLensingSpectra['cl_phiphi'])[0]:
print(('Need to supply lensing spectrum to lmax >= ' + str( dcode['l_max_scalars'] + (dcode['delta_l_max']+dcode['delta_dl_max'])+1 ) + \
' in order to compute delensed spectrum to lmax = ' + str( lmax ) ))
stop
dcode['lensed cmb spectra type'] = 'external'
np.savetxt(classDataDir + 'input/' + rootName + '_lensed_input_spectra.dat', \
np.column_stack((externalLensedCMBSpectra['l'], externalLensedCMBSpectra['cl_TT'], \
externalLensedCMBSpectra['cl_TE'], externalLensedCMBSpectra['cl_EE'], \
externalLensedCMBSpectra['cl_BB'], externalLensingSpectra['cl_phiphi'] )) )
dcode['command_for_external_lensed_cmb_spectra'] = 'cat ' + classDataDir + 'input/' + rootName + '_lensed_input_spectra.dat'
########################################################
## CLASS code calculates most of the relevant data, ##
## including the spectra, reconstruction noise and ##
## derivatives. It is important to give the correct ##
## input file and with appropriate options included ##
########################################################
if calculateDerivatives is not False:
dcode['delensing derivatives'] = 'yes'
dcode['output_derivatives'] = 'yes'
dcode['derv_binedges'] = '1'
dervtype = calculateDerivatives
dcode['derivative type'] = dervtype
if includeUnlensedSpectraDerivatives is not False:
dcode['calculate_derviaties_wrt_unlensed'] = 'yes'
dcode['unlensed derivative type'] = dervtype
## extraParams is dictionary allowing for arbitrary additional specifications in CLASS format
## e.g. extraParams['tol_ncdm'] = 1.e-3 sets a precision parameter associated with ncdm
## extraParams will overwrite any previously set parameters
if backgroundOnly is True:
cosmoclass['write_background'] = 'yes'
cosmoclass['write_thermodynamics'] = 'yes'
cosmoclass['thermodynamics_verbose'] = 1
dcode['output'] = ''
dcode['lensing'] = 'no'
dcode['non linear'] = ''
calculateDerivatives = False
dcode.update(extraParams)
########################################################
## Running the CLASS code to write the spectra inside ##
## specific files, as well as their derivatives, and ##
## the CMB and lensing noise used to create these. ##
########################################################
with open(classDataDir + 'input/' + rootName + ".ini", "w") as f:
f.write( '\n'.join(['%s = %s' % (k, v) for k, v in list(cosmoclass.items())])
+'\n'
+'\n'.join(['%s = %s' % (k, v) for k, v in list(dcode.items())]))
os.system("cd " + classExecDir + " ; ./class " + classDataDir + 'input/' + rootName + ".ini")
########################################################
## Filling the spectra into specific CLASS dict()s. ##
########################################################
if calculateDerivatives is False and backgroundOnly is False:
########################################################
## the calculated spectra to be filled in whats below ##
########################################################
cspec = dict()
cspec['cl_unlensed'] = None # To be filled with unlensed CMB spectra
cspec['cl_lensed'] = None # To be filled with lensed CMB spectra
cspec['cl_delensed'] = None # To be filled with delensed CMB spectra
cspec['nl_lensing'] = None # To be filled with lensing reconstruction noise
cspec['nl_cmb'] = None # To be filled with CMB spectra noise
cspec['cl_unlensed'] = np.loadtxt(classDataDir + "output/" + rootName + "_cl.dat")
cspec['cl_lensed'] = np.loadtxt(classDataDir + "output/" + rootName + "_cl_lensed.dat")
cspec['cl_delensed'] = np.loadtxt(classDataDir + "output/" + rootName + "_cl_delensed.dat")
cspec['nl_cmb'] = np.loadtxt(classDataDir + "output/" + rootName + "_spectra_noise.dat")
if deflectionNoise is None:
cspec['nl_lensing'] = np.loadtxt(classDataDir + "output/" + rootName + "_lensing_noise_rcn.dat")
lvec = cspec['cl_lensed'][:,0]
nElls = len(lvec)
########################################################
## Filling dict()s in a way similar to the JM wrapper ##
########################################################
dl_TT = cspec['cl_unlensed'][:nElls,1]*(TCMB*1.e6)**2
dl_EE = cspec['cl_unlensed'][:nElls,2]*(TCMB*1.e6)**2
dl_TE = cspec['cl_unlensed'][:nElls,3]*(TCMB*1.e6)**2
dl_BB = cspec['cl_unlensed'][:nElls,4]*(TCMB*1.e6)**2
cl_TT = cspec['cl_unlensed'][:nElls,1]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_EE = cspec['cl_unlensed'][:nElls,2]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_TE = cspec['cl_unlensed'][:nElls,3]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_BB = cspec['cl_unlensed'][:nElls,4]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
unlensed = {'l' : cspec['cl_unlensed'][:nElls,0],
'cl_TT' : cl_TT,
'cl_EE' : cl_EE,
'cl_TE' : cl_TE,
'cl_BB' : cl_BB,
'dl_TT' : dl_TT,
'dl_EE' : dl_EE,
'dl_TE' : dl_TE,
'dl_BB' : dl_BB
}
dl_TT_lensed = cspec['cl_lensed'][:nElls,1]*(TCMB*1.e6)**2
dl_EE_lensed = cspec['cl_lensed'][:nElls,2]*(TCMB*1.e6)**2
dl_TE_lensed = cspec['cl_lensed'][:nElls,3]*(TCMB*1.e6)**2
dl_BB_lensed = cspec['cl_lensed'][:nElls,4]*(TCMB*1.e6)**2
cl_TT_lensed = cspec['cl_lensed'][:nElls,1]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_EE_lensed = cspec['cl_lensed'][:nElls,2]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_TE_lensed = cspec['cl_lensed'][:nElls,3]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_BB_lensed = cspec['cl_lensed'][:nElls,4]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
lensed = {'l' : cspec['cl_lensed'][:nElls,0],
'cl_TT' : cl_TT_lensed,
'cl_EE' : cl_EE_lensed,
'cl_TE' : cl_TE_lensed,
'cl_BB' : cl_BB_lensed,
'dl_TT' : dl_TT_lensed,
'dl_EE' : dl_EE_lensed,
'dl_TE' : dl_TE_lensed,
'dl_BB' : dl_BB_lensed
}
dl_TT_delensed = cspec['cl_delensed'][:nElls,1]*(TCMB*1.e6)**2
dl_EE_delensed = cspec['cl_delensed'][:nElls,2]*(TCMB*1.e6)**2
dl_TE_delensed = cspec['cl_delensed'][:nElls,3]*(TCMB*1.e6)**2
dl_BB_delensed = cspec['cl_delensed'][:nElls,4]*(TCMB*1.e6)**2
cl_TT_delensed = cspec['cl_delensed'][:nElls,1]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_EE_delensed = cspec['cl_delensed'][:nElls,2]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_TE_delensed = cspec['cl_delensed'][:nElls,3]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
cl_BB_delensed = cspec['cl_delensed'][:nElls,4]*2*np.pi/(lvec*(lvec+1))*(TCMB*1.e6)**2
delensed = {'l' : cspec['cl_delensed'][:nElls,0],
'cl_TT' : cl_TT_delensed,
'cl_EE' : cl_EE_delensed,
'cl_TE' : cl_TE_delensed,
'cl_BB' : cl_BB_delensed,
'dl_TT' : dl_TT_delensed,
'dl_EE' : dl_EE_delensed,
'dl_TE' : dl_TE_delensed,
'dl_BB' : dl_BB_delensed
}
cl_TT_noise = cspec['nl_cmb'][:nElls,1]
cl_EE_noise = cspec['nl_cmb'][:nElls,2]
cl_TE_noise = np.empty(nElls)
cl_TE_noise.fill(0.)
cl_BB_noise = cspec['nl_cmb'][:nElls,2]
dl_TT_noise = cl_TT_noise*(lvec*(lvec+1))/(2*np.pi)
dl_EE_noise = cl_EE_noise*(lvec*(lvec+1))/(2*np.pi)
dl_TE_noise = cl_TE_noise*(lvec*(lvec+1))/(2*np.pi)
dl_BB_noise = cl_BB_noise*(lvec*(lvec+1))/(2*np.pi)
cmbNoiseSpectra = {'l' : cspec['nl_cmb'][:nElls,0],
'cl_TT' : cl_TT_noise,
'cl_EE' : cl_EE_noise,
'cl_TE' : cl_TE_noise,
'cl_BB' : cl_BB_noise,
'dl_TT' : dl_TT_noise,
'dl_EE' : dl_EE_noise,
'dl_TE' : dl_TE_noise,
'dl_BB' : dl_BB_noise
}
cl_phiphi = cspec['cl_unlensed'][:nElls,5]*2*np.pi/(lvec*(lvec+1))
cl_dd = cl_phiphi*(lvec*(lvec+1))
cl_kk = cl_phiphi*(lvec*(lvec+1))*(lvec*(lvec+1))/4.
lensing = {'l' : cspec['cl_unlensed'][:nElls,0],
'cl_phiphi' : cl_phiphi,
'cl_dd' : cl_dd,
'cl_kk' : cl_kk
}
powers=dict()
powers['unlensed'] = unlensed
powers['lensed'] = lensed
powers['lensing'] = lensing
powers['delensed'] = delensed
## These lines are used to avoid error when not computing deflection noise
deflection_noise = dict()
deflection_noise['MV'] = None
if deflectionNoise is None:
if dcode['min_varr_type'] == 'diag':
nl_dd_MV = cspec['nl_lensing'][:nElls,1]*2.*np.pi
nl_dd_TT = cspec['nl_lensing'][:nElls,2]*2.*np.pi
nl_dd_TE = cspec['nl_lensing'][:nElls,3]*2.*np.pi
nl_dd_EE = cspec['nl_lensing'][:nElls,4]*2.*np.pi
nl_dd_BB = cspec['nl_lensing'][:nElls,5]*2.*np.pi
nl_dd_EB = cspec['nl_lensing'][:nElls,6]*2.*np.pi
nl_dd_TB = cspec['nl_lensing'][:nElls,7]*2.*np.pi
deflection_noise = {'l' :cspec['nl_lensing'][:nElls,0],
'MV' : nl_dd_MV,
'TT' : nl_dd_TT,
'TE' : nl_dd_TE,
'EE' : nl_dd_EE,
'BB' : nl_dd_BB,
'EB' : nl_dd_EB,
'TB' : nl_dd_TB
}
elif dcode['min_varr_type'] == 'eb':
nl_dd_MV = cspec['nl_lensing'][:nElls,1]*2.*np.pi
nl_dd_EB = cspec['nl_lensing'][:nElls,2]*2.*np.pi
deflection_noise = {'l' :cspec['nl_lensing'][:nElls,0],
'MV' : nl_dd_MV,
'EB' : nl_dd_EB
}
if outputAllReconstructions is False:
reconstructionOutput = deflection_noise['MV']
if outputAllReconstructions is True:
reconstructionOutput = deflection_noise
if calculateDerivatives is not False:
########################################################
## Calculate the derivatives of the CMB spectra. ##
########################################################
dCldCLd = loadLensingDerivatives(rootName = rootName,
classDataDir = classDataDir,
dervtype = dervtype)
if includeUnlensedSpectraDerivatives is not False:
dCldCLu = loadUnlensedSpectraDerivatives(rootName = rootName,
classDataDir = classDataDir,
dervtype = dervtype)
if backgroundOnly is True:
# returns rs/dV for desired redshifts
background = np.loadtxt(classDataDir + "output/" + rootName + "_background.dat")
thermo = np.loadtxt(classDataDir + "output/" + rootName + "_thermodynamics.dat")
thermo_summary = np.loadtxt(classDataDir + "output/" + rootName + "_thermodynamics_summary.dat")
output_data = {'background' : background,
'thermo' : thermo,
'thermo_summary' : thermo_summary}
output = output_data
elif calculateDerivatives is False:
output = [powers, reconstructionOutput]
else:
if includeUnlensedSpectraDerivatives is not False:
output = [dCldCLd, dCldCLu]
else:
output = dCldCLd
return output
# CST
def camb_class_generate_data(cosmo,
rootName = 'testing',
cmbNoise = None,
noiseLevel = 1.,
beamSizeArcmin = 1.,
deflectionNoise = None,
externalUnlensedCMBSpectra = None,
externalLensedCMBSpectra = None,
externalLensingSpectra = None,
classExecDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
calculateDerivatives = False,
includeUnlensedSpectraDerivatives = False,
outputAllReconstructions = False,
reconstructionMask = None,
lmax = 5000,
extraParams = dict(),
accuracy = 2,
doLensedWithCAMB = False):
cambPowerSpectra = cambWrapTools.getPyCambPowerSpectra(cosmo = cosmo, \
accuracy = accuracy, \
lmaxToWrite = lmax+3000)
if calculateDerivatives == False:
powersFid, deflectionNoises = class_generate_data(cosmo = cosmo,
rootName = rootName,
cmbNoise = cmbNoise,
noiseLevel = noiseLevel,
beamSizeArcmin = beamSizeArcmin,
deflectionNoise = deflectionNoise,
externalUnlensedCMBSpectra = cambPowerSpectra['unlensed'],
externalLensedCMBSpectra = externalLensedCMBSpectra,
externalLensingSpectra = cambPowerSpectra['lensing'],
classExecDir = classExecDir,
classDataDir = classDataDir,
calculateDerivatives = calculateDerivatives,
includeUnlensedSpectraDerivatives = includeUnlensedSpectraDerivatives,
outputAllReconstructions = outputAllReconstructions,
reconstructionMask = reconstructionMask,
lmax = lmax,
extraParams = extraParams)
powersFid['unlensed'] = cambPowerSpectra['unlensed']
## If you want to use the lensed spectra from CAMB
if doLensedWithCAMB == True:
powersFid['lensed'] = cambPowerSpectra['lensed']
powersFid['lensing'] = cambPowerSpectra['lensing']
return powersFid, deflectionNoises
else:
return class_generate_data(cosmo = cosmo,
rootName = rootName,
cmbNoise = cmbNoise,
noiseLevel = noiseLevel,
beamSizeArcmin = beamSizeArcmin,
deflectionNoise = deflectionNoise,
externalUnlensedCMBSpectra = cambPowerSpectra['unlensed'],
externalLensedCMBSpectra = externalLensedCMBSpectra,
externalLensingSpectra = cambPowerSpectra['lensing'],
classExecDir = classExecDir,
classDataDir = classDataDir,
calculateDerivatives = calculateDerivatives,
includeUnlensedSpectraDerivatives = includeUnlensedSpectraDerivatives,
outputAllReconstructions = outputAllReconstructions,
reconstructionMask = reconstructionMask,
lmax = lmax,
extraParams = extraParams)
def generate_data(cosmo,
rootName = 'testing',
cmbNoise = None,
noiseLevel = 1.,
beamSizeArcmin = 1.,
deflectionNoise = None,
externalUnlensedCMBSpectra = None,
externalLensedCMBSpectra = None,
externalLensingSpectra = None,
classExecDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
calculateDerivatives = False,
includeUnlensedSpectraDerivatives = False,
outputAllReconstructions = False,
reconstructionMask = None,
lmax = 5000,
extraParams = dict(),
accuracy = 2,
useClass = True,
doLensedWithCAMB = False):
if useClass == True:
return class_generate_data(cosmo,
rootName = rootName,
cmbNoise = cmbNoise,
noiseLevel = noiseLevel,
beamSizeArcmin = beamSizeArcmin,
deflectionNoise = deflectionNoise,
externalUnlensedCMBSpectra = externalUnlensedCMBSpectra,
externalLensedCMBSpectra = externalLensedCMBSpectra,
externalLensingSpectra = externalLensingSpectra,
classExecDir = classExecDir,
classDataDir = classDataDir,
calculateDerivatives = calculateDerivatives,
includeUnlensedSpectraDerivatives = includeUnlensedSpectraDerivatives,
outputAllReconstructions = outputAllReconstructions,
reconstructionMask = reconstructionMask,
lmax = lmax,
extraParams = extraParams
)
elif useClass == False:
return camb_class_generate_data(cosmo,
rootName = rootName,
cmbNoise = cmbNoise,
noiseLevel = noiseLevel,
beamSizeArcmin = beamSizeArcmin,
deflectionNoise = deflectionNoise,
externalUnlensedCMBSpectra = externalUnlensedCMBSpectra,
externalLensedCMBSpectra = externalLensedCMBSpectra,
externalLensingSpectra = externalLensingSpectra,
classExecDir = classExecDir,
classDataDir = classDataDir,
calculateDerivatives = calculateDerivatives,
includeUnlensedSpectraDerivatives = includeUnlensedSpectraDerivatives,
outputAllReconstructions = outputAllReconstructions,
reconstructionMask = reconstructionMask,
lmax = lmax,
extraParams = extraParams,
accuracy = accuracy,
doLensedWithCAMB = doLensedWithCAMB
)
def loadLensingDerivatives(rootName = 'testing',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/../../CLASS_delens/',
dervtype = 'lensed'):
## Loads dCldCLd from files as computed by class_generate_data and returns dictionary with zero padding as used by Fisher code ##
dCldCLd = dict()
dCldCLd['cl_TT'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClTTdCldd_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLd['cl_EE'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClEEdCldd_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLd['cl_TE'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClTEdCldd_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLd['cl_BB'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClBBdCldd_"+dervtype+".dat"), ((2,0),), 'constant')
return dCldCLd
def loadUnlensedSpectraDerivatives(rootName = 'testing',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/../../CLASS_delens/',
dervtype = 'lensed'):
## Loads dCldCLu from files as computed by class_generate_data and returns dictionary with zero padding as used by Fisher code ##
dCldCLu = dict()
dCldCLu['cl_TT_cl_TT'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClTTdClTT_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLu['cl_TE_cl_TE'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClTEdClTE_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLu['cl_EE_cl_EE'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClEEdClEE_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLu['cl_EE_cl_BB'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClEEdClBB_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLu['cl_BB_cl_EE'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClBBdClEE_"+dervtype+".dat"), ((2,0),), 'constant')
dCldCLu['cl_BB_cl_BB'] = np.pad(np.loadtxt(classDataDir + "output/" + rootName + "_dClBBdClBB_"+dervtype+".dat"), ((2,0),), 'constant')
return dCldCLu
###########################
## Copied from biasespol ##
###########################
def noiseSpectra(l, noiseLevelT, useSqrt2 = True, beamArcmin = 1.4, beamFile = None, noiseLevelP = None):
#make a full set of noise spectra.
if beamFile == None:
beam_sigma_radians = (beamArcmin * np.pi / (180. * 60.)) / np.sqrt(8. * np.log(2) )
beamPower = np.exp(l * (l+1) * beam_sigma_radians**2)
else:
beamVals = np.loadtxt(beamFile)
beamValsOnL = (scipy.interpolate.interp1d(beamVals[:,0], beamVals[:,1], bounds_error = False))(l)
beamPower = 1/(beamValsOnL**2)
noise_ster = (np.pi / (180. * 60))**2 * noiseLevelT**2
nl = len(l)
cl_TT = np.empty(nl)
cl_TT.fill(noise_ster)
cl_TT *= beamPower
cl_EE = np.empty(nl)
if useSqrt2:
cl_EE.fill(noise_ster * 2.)
else:
noise_sterP = (np.pi / (180. * 60))**2 * noiseLevelP**2
cl_EE.fill(noise_sterP)
cl_EE *= beamPower
cl_BB = np.empty(nl)
if useSqrt2:
cl_BB.fill(noise_ster * 2.)
else:
noise_sterP = (np.pi / (180. * 60))**2 * noiseLevelP**2
cl_BB.fill(noise_sterP)
cl_BB *= beamPower
cl_TE = np.empty(nl)
cl_TE.fill(0.)
output = {'l' : l,\
'cl_TT' : cl_TT,\
'cl_EE' : cl_EE,\
'cl_TE' : cl_TE,\
'cl_BB' : cl_BB,
'dl_TT' : cl_TT * l * (l + 1) / 2 / np.pi,\
'dl_EE' : cl_EE * l * (l + 1) / 2 / np.pi,\
'dl_TE' : cl_TE * l * (l + 1) / 2 / np.pi,\
'dl_BB' : cl_BB * l * (l + 1) / 2 / np.pi
}
return output
def getBAOParams(cosmo,
redshifts,
rootName = 'testing_bao',
classExecDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
classDataDir = os.path.dirname(os.path.abspath(__file__)) + '/CLASS_delens/',
extraParams = dict(),
):
# returns rs/dV for desired redshifts
bg_data = class_generate_data(cosmo = cosmo,
rootName = rootName,
classExecDir = classExecDir,
classDataDir = classDataDir,
backgroundOnly = True,
extraParams = extraParams
)
rs = bg_data['thermo_summary'][-1]
zs = bg_data['background'][:,0]
ang_diam_dist = bg_data['background'][:,5]
Hz = bg_data['background'][:,3]
c = 1.
rs_dV = rs/(((c)*zs*((1+zs) ** 2.) * (ang_diam_dist ** 2.) * (Hz ** -1.)) ** (1./3))
return np.interp(redshifts,zs[::-1],rs_dV[::-1])