Computers and software • Problem with camb.correlations.cl2corr function

Code:

import matplotlib.pyplot as pltimport matplotlib.colors as colorsimport cambimport numpy as npimport pandas as pdimport scipy unbin_cl = pd.read_csv('maps/COM_PowerSpect_CMB-TT-full_R3.01.txt', sep=',')unbin_cl.head()unbin_cl.columns = ['ell', 'D_ell', '-dD_ell', '+dD_ell']unbin_cl.head()def solidang(cor,theta):    from scipy.integrate import simpson    # Calcular sin(theta)    sin_theta = np.sin(theta)    integrand = cor[:,0] * sin_theta    # Integrar usando la regla del trapecio    integral = simpson(integrand, theta)    # Multiplicar por 2*pi para obtener la integral sobre el ángulo sólido    integral_solido = 2 * np.pi * integral    return round(integral_solido,3)def clcorr(cls, c= False):    import camb.correlations    xvals = np.linspace(-0.999,0.999,1000)    theta=np.arccos(xvals)*180/np.pi    if c:                corrs = camb.correlations.cl2corr(cls,xvals)        inte= solidang(corrs,theta)        return corrs[:,0], theta, inte    else:        clstot = np.tile(np.array(cls).reshape(-1, 1), 4)        corrs = camb.correlations.cl2corr(clstot,xvals)        inte= solidang(corrs, theta)        return corrs[:,0], theta, intedef clandcorrplotbase(file, expdata=unbin_cl):    from scipy.stats import chisquare    data = pd.read_csv(f'COM_CosmoParams_fullGrid_R3.01/base/{file}/base_{file}.minimum.theory_cl', sep=r'\s+', skiprows=1, header=None)    data.columns = ["L", "TT", "TE", "EE", "BB", "PP"]    #print(data.head())    # Define the list of parameter names to keep    params_to_keep = {"omegabh2", "omegach2", "H0", "tau", "omegak", "logA", "ns",                     "mnu", "w", "wa", "nnu", "yhe", "Alens", "nrun"}    # Read the file while skipping LaTeX notation at the end    df = pd.read_csv(f'COM_CosmoParams_fullGrid_R3.01/base/{file}/base_{file}.minimum', sep=r"\s+", header=None, usecols=[1, 2], names=["value", "param"])    # Filter rows where the 'param' column matches the parameters we want    df_filtered = df[df["param"].isin(params_to_keep)]    # Display the filtered dataframe    df_filtered['value']=df_filtered['value'].astype(float)    dic = {}    for i, j in zip(df_filtered['param'],df_filtered['value']):        dic[i]= j    #print(dic)    pars = camb.set_params(ombh2=dic['omegabh2'], omch2=dic['omegach2'], H0 = dic['H0'],omk=dic['omegak'],                            YHe=dic['yhe'], nnu=dic['nnu'], nrun=dic['nrun'], Alens=dic['Alens'], ns=dic['ns'], As=np.exp(dic['logA'])*1e-10,w=dic['w'],wa=dic['wa'], mnu=dic['mnu'], tau=dic['tau'])    #res = camb.get_background(pars, no_thermo=True)    resu = camb.get_results(pars)    cls = resu.get_cmb_power_spectra(pars, CMB_unit='muK',lmax=max(data['L']))    totCL=cls['total']    ell = expdata['ell'][:len(data['L'])]    D_ell = expdata['D_ell'][:len(data['L'])]    errors = (expdata['-dD_ell'][:len(data['L'])],expdata['+dD_ell'][:len(data['L'])])    fig, axes = plt.subplots(6, 2, sharex='col', gridspec_kw={'height_ratios': [3, 1,3, 1,3, 1]})    fig.subplots_adjust(hspace=0)    fig.set_size_inches(60,15)    # Experimental vs theory cl    expected_th = data['TT'] * (D_ell.sum() / data['TT'].sum())        mask = expected_th != 0    D_ell_filtered = D_ell[mask]    expected_th_filtered = expected_th[mask]    # Normalize expected values to match observed values' sum    expected_th_filtered *= D_ell_filtered.sum() / expected_th_filtered.sum()    # Compute chi-square    chi_th, _ = chisquare(D_ell_filtered, expected_th_filtered)    axes[0,0].scatter(ell,data['TT'], marker="+", c='black', s=5, label='Best fit')    axes[0,0].errorbar(ell, D_ell, yerr=errors, fmt='o', color='red', ecolor=colors.to_rgba('blue',0.2), elinewidth=1, capsize=2, markersize=2, label=rf"Planck data $\chi_n^2={round(chi_th,3)/len(D_ell[mask])}$")    axes[0,0].set_ylabel(r'$D_\ell^{TT} \, [\mu K^2]$')    # Bottom subplot (Residuals)    delta_D_ell = np.array(D_ell) - np.array(data['TT'])    axes[1,0].errorbar(data['L'], delta_D_ell, yerr=errors, fmt='o', color='green', ecolor=colors.to_rgba('blue',0.2), elinewidth=1, capsize=2, markersize=2, label="Residuals")    axes[1,0].set_ylabel(r'$\Delta D_\ell^{TT}$')        axes[0,0].title.set_text("Power Spectrum of TT from Planck Archive Unbinned")    tcor, theta   ,_= clcorr(data[['TT','TE','EE','BB']],True)    expcor, theta ,_ = clcorr(D_ell)    axes[0,1].scatter(theta,tcor, marker="+", c='black', s=2, label='Derived from best fit')    axes[0,1].scatter(theta,expcor, marker="o", c='red', s=2, label='Derived from Planck data')    axes[0,1].title.set_text("Correlation function")    delta_corr=np.array(expcor) - np.array(tcor)    axes[1,1].scatter(theta,delta_corr,marker="^", c='green', s=2, label="Residuals")    axes[1,1].set_ylabel(r'$\Delta \xi (\theta)$')    axes[0,1].set_ylabel(r'$\xi (\theta)$')    # Theory vs CAMb    axes[2,0].scatter(ell,data['TT'], marker="+", c='black', s=5, label='Best fit')    axes[2,0].scatter(ell,totCL[:,0][2:], marker="o", c='red', s=5, label='CAMB results')    axes[2,0].set_ylabel(r'$D_\ell^{TT} \, [\mu K^2]$')    # Bottom subplot (Residuals)    delta_D_ell = np.array(totCL[:,0][2:]) - np.array(data['TT'])    axes[3,0].errorbar(data['L'], delta_D_ell, yerr=errors, fmt='o', color='green', ecolor=colors.to_rgba('blue',0.2), elinewidth=1, capsize=2, markersize=2, label="Residuals")    axes[3,0].set_ylabel(r'$\Delta D_\ell^{TT}$')    axes[3,0].set_xlabel(r'$\ell$')    tcor, theta   , inte_bf= clcorr(data[['TT','TE','EE','BB']],True)    cambcor, theta, inte_camb = clcorr(totCL,True)    axes[2,1].scatter(theta,tcor, marker="+", c='black', s=2, label='Derived from best fit')    axes[2,1].scatter(theta,cambcor, marker="o", c='red', s=2, label='Derived from CAMB')    delta_corr=np.array(cambcor) - np.array(tcor)    axes[3,1].scatter(theta,delta_corr,marker="^", c='green', s=2, label="Residuals")    axes[3,1].set_ylabel(r'$\Delta \xi (\theta)$')    axes[2,1].set_ylabel(r'$\xi (\theta)$')    # CAMB vs experimental    expected_camb = totCL[:,0][2:] * (D_ell.sum() / totCL[:,0][2:].sum())    # Apply mask    mask2 = expected_camb != 0    D_ell_filtered = D_ell[mask2]    expected_camb_filtered = expected_camb[mask2]    # Normalize expected values to match observed values' sum    expected_camb_filtered *= D_ell_filtered.sum() / expected_camb_filtered.sum()    # Compute chi-square    chi_camb, _ = chisquare(D_ell_filtered, expected_camb_filtered)    print(f"Excluding {len(D_ell)-len(D_ell[mask])} data points")    print(f"Excluding {len(D_ell)-len(D_ell[mask2])} data points")        axes[4,0].scatter(ell,totCL[:,0][2:], marker="o", c='black', s=5, label='CAMB results')    axes[4,0].errorbar(ell, D_ell, yerr=errors, fmt='o', color='red', ecolor=colors.to_rgba('blue',0.2), elinewidth=1, capsize=2, markersize=2, label=rf"Planck data $\chi_n^2={round(chi_camb,3)/len(D_ell[mask2])}$")    axes[4,0].set_ylabel(r'$D_\ell^{TT} \, [\mu K^2]$')    # Bottom subplot (Residuals)    delta_D_ell = np.array(D_ell) - np.array(totCL[:,0][2:])    axes[5,0].errorbar(data['L'], delta_D_ell, yerr=errors, fmt='o', color='green', ecolor=colors.to_rgba('blue',0.2), elinewidth=1, capsize=2, markersize=2, label="Residuals")    axes[5,0].set_ylabel(r'$\Delta D_\ell^{TT}$')    axes[5,0].set_xlabel(r'$\ell$')    expcor, theta , inte_exp = clcorr(D_ell)    cambcor, theta,_ = clcorr(totCL,True)    axes[4,1].scatter(theta,expcor, marker="+", c='red', s=2, label='Derived from Plancks Data')    axes[4,1].scatter(theta,cambcor, marker="o", c='black', s=2, label='Derived from CAMB')    delta_corr=np.array(expcor) - np.array(cambcor)    axes[5,1].scatter(theta,delta_corr,marker="^", c='green', s=2, label="Residuals")    axes[5,1].set_xlabel(r'$\theta [^\circ]$')    axes[5,1].set_ylabel(r'$\Delta \xi (\theta)$')    axes[4,1].set_ylabel(r'$\xi (\theta)$')    fig.text(0.51, 0.82, "Experimenatl VS Bestfit\n" + r"$\int \xi(\theta)_{exp}d\Omega =$" + f"{inte_exp}",          ha='center', fontsize=12, fontweight='bold')    fig.text(0.51, 0.52, "CAMB Best VS Bestfit\n" + r"$\int \xi(\theta)_{bf}d\Omega =$" + f"{inte_bf}",             ha='center', fontsize=12, fontweight='bold')    fig.text(0.51, 0.22, "Experimenatl VS CAMB Best\n" + r"$\int \xi(\theta)_{CAMB}d\Omega =$" + f"{inte_camb}",             ha='center', fontsize=12, fontweight='bold')    title = f"Comparision of plancks result for the chain {file}\n"    for i in params_to_keep:        title+=f"{i}: {dic[i]}  "    fig.suptitle(title)    for ax in axes[:,0]:        ax.axvline(x=20, color='black', linestyle='--', linewidth=0.5)        ax.set_yscale('linear')        ax.grid(True, which='both', linestyle='--', linewidth=0.5)        ax.legend(loc='upper right')        ax.set_xscale('log')    for ax in axes[:,1]:        ax.set_xscale('linear')        ax.set_xlim(0,max(theta)+0.5)        ax.set_yscale('linear')        ax.grid(True, which='both', linestyle='--', linewidth=0.5)        ax.legend(loc='upper right')    plt.legend()    plt.savefig(f"Comp_Graphs/base/{file}.png")    plt.close()

Code:

def clcorr(dls, c= False):        from scipy.special import eval_legendre    xvals = np.linspace(-0.999,0.999,1000)    theta=np.arccos(xvals)*180/np.pi        if c:        cls=dls[:,0]        lmax=len(cls)        corr= np.zeros(dtype=np.float64, shape=xvals.shape)        ell = np.linspace(2,lmax+2,lmax)        cl=cls*2*np.pi*1/(ell*(ell+1))        for i ,j in enumerate(cl):            deg=i+2            P_n = eval_legendre(deg, xvals)            corr += (2*deg+1)*j*P_n        corrs = 1/(4*np.pi)*corr        inte= solidang(corrs,theta)        return corrs, theta, inte    else:        cls=dls        lmax=len(cls)        corr= np.zeros(dtype=np.float64, shape=xvals.shape)        ell = np.linspace(2,lmax+2,lmax)        cl=cls*2*np.pi*1/(ell*(ell+1))        for i ,j in enumerate(cl):            deg=i+2            P_n = eval_legendre(deg, xvals)            corr += (2*deg+1)*j*P_n        corrs = 1/(4*np.pi)*corr        inte= solidang(corrs, theta)        return corrs, theta, inte