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# You can also calculate CMB correlation functions
+# (the correlations module also has useful functions for the inverse transform,
+# CMB lensing and derivatives of the lensed spectra - see docs)
+from camb import correlations
+
+pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.96)
+pars.set_for_lmax(4000, lens_potential_accuracy=1)
+results = camb.get_results(pars)
+lensed_cl = results.get_lensed_scalar_cls()
+corrs, xvals, weights = correlations.gauss_legendre_correlation(lensed_cl)
+
+r=np.arccos(xvals)*180/np.pi # sampled theta values in degrees
+fig, axs= plt.subplots(2,2, figsize=(12,8))
+for ix, (cap, ax) in enumerate(zip(['TT','+','-',r'\times'], axs.reshape(4))):
+ ax.plot(r, corrs[:,ix])
+ ax.axhline(0,color='k')
+ ax.set_xlim([0,10])
+ ax.set_xlabel(r'$\theta$')
+ ax.set_ylabel(r'$\zeta_{%s}(\theta)$'%(cap), fontsize=14)
+ ax.set_xlim(0,4)
+plt.suptitle('Correlation functions');
+
+# For partially-delensed spectra, power spectra can be computed using a custom
+# or scaled lensing potential power spectrum. There's a pure-python interface
+# in the correlation module, or can use the result object functions (faster).
+# Here just plot results for scaled lensing spectrum, can use
+# get_lensed_cls_with_spectrum to calculate lensed spectrum with specific
+# lensing power if needed
+
+pars = camb.set_params(H0=67.5, ombh2=0.022, omch2=0.122, As=2e-9, ns=0.96)
+pars.set_for_lmax(3500, lens_potential_accuracy=1)
+results = camb.get_results(pars)
+for Alens in [1,0.9, 0.5, 0.1]:
+ plt.plot(np.arange(2,2501),
+ results.get_partially_lensed_cls(Alens, lmax=2500)[2:,2],
+ label='$A_L = %s$'%Alens)
+plt.ylabel(r'$C_l^{BB}$')
+plt.xlabel('$l$')
+plt.xlim([2,2500])
+plt.legend();
+
+