lib.Fitting.Qcamb module

lib.Fitting.Qcamb.Dl2Cl_without_monopole(ls, totDL)

Go from Dls to Cls.

lib.Fitting.Qcamb.bin_camblib(Namaster, filename, nside, verbose=True, return_unbinned=False)

Bin the spectra using Namaster. :type Namaster: :param Namaster: :type filename: :param filename: :type nside: :param nside: :type verbose: :param verbose: :type return_unbinned: :param return_unbinned:

lib.Fitting.Qcamb.cell_2_ctheta(cell, theta_deg=None, normalization=1.0)

lib.Fitting.Qcamb.ctheta_2_cell(theta_deg, ctheta, lmax, normalization=1.0)

lib.Fitting.Qcamb.get_Dl_fromlib(lvals, r, lib=None, specindex=None, unlensed=False)

lib.Fitting.Qcamb.get_camb_Dl(lmax=2500, H0=67.5, ombh2=0.022, omch2=0.122, mnu=0.06, omk=0, tau=0.06, As=2e-09, ns=0.965, r=0.0)

Inspired from: https://camb.readthedocs.io/en/latest/CAMBdemo.html NB: this returns Dl = l(l+1)Cl/2pi Python CL arrays are all zero based (starting at l=0), Note l=0,1 entries will be zero by default. The different DL are always in the order TT, EE, BB, TE (with BB=0 for unlensed scalar results).

lib.Fitting.Qcamb.rcamblib(rvalues, lmax=768, save=None)

Make CAMB library

lib.Fitting.Qcamb.read_camblib(file)

lib.Fitting.Qcamb.simulate_correlated_map(nside, signoise, clin=None, nside_fact=1, lmax_nside=2.0, generate_alm=False, verbose=True, myiter=3, use_weights=False, seed=None, synfast=True)