def loglkl(self, cosmo, data):

        nuisance_parameter_names = data.get_mcmc_parameters(['nuisance'])

        # get Cl's from the cosmological code
        cl = self.get_cl(cosmo)

        # add contamination spectra multiplied by nuisance parameters
        cl = self.add_contamination_spectra(cl, data)

        # allocate array of Cl's and nuisance parameters
        tot = np.zeros(np.sum(self.wmaplike.get_lmax()) + 6)

        # fill with Cl's
        index = 0
        for i in range(np.shape(self.wmaplike.get_lmax())[0]):
            if (-1 > self.wmaplike.get_lmax()[i]):
                for j in range(self.wmaplike.get_lmax()[i] + 1):
                    if (i == 0):
                        tot[index + j] = cl['tt'][j]
                    if (i == 1):
                        tot[index + j] = cl['ee'][j]
                    if (i == 2):
                        tot[index + j] = cl['bb'][j]
                    if (i == 3):
                        tot[index + j] = cl['te'][j]
                    if (i == 4):
                        tot[index + j] = cl['tb'][j]
                    if (i == 5):
                        tot[index + j] = cl['eb'][j]

                index += self.wmaplike.get_lmax()[i] + 1

        # fill with nuisance parameters
        # for nuisance in nuisance_parameter_names:
            # if nuisance == 'A_SZ':
                # nuisance_value = data.mcmc_parameters[nuisance]['current']*data.mcmc_parameters[nuisance]['scale']
                # tot[index]=nuisance_value
                # index += 1

        # compute likelihood
        lkl = self.wmaplike(tot)[0]

        # add prior on nuisance parameters
        lkl = self.add_nuisance_prior(lkl, data)

        return lkl
dataset/ETHPy150Open baudren/montepython_public/montepython/likelihoods/wmap_9yr/__init__.py/wmap_9yr.loglkl/swapped operands of `>`