How to use the treecorr.corr2 function in TreeCorr

for i in range(nrand):
            fid.write(('%.20f %.20f\n')%(rx1[i],ry1[i]))
    rand_file_name2 = os.path.join('data','nn_periodic_rand2.dat')
    with open(rand_file_name2, 'w') as fid:
        for i in range(nrand):
            fid.write(('%.20f %.20f\n')%(rx2[i],ry2[i]))
    rr = treecorr.NNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, bin_slop=0,
                                verbose=0, xperiod=Lx, yperiod=Ly)
    rr.process(rcat1,rcat2, metric='Periodic')
    xi, varxi = dd.calculateXi(rr)
    print('xi = ',xi)

    # Do this via the corr2 function.
    config = treecorr.config.read_config('configs/nn_periodic.yaml')
    logger = treecorr.config.setup_logger(2)
    treecorr.corr2(config, logger)
    corr2_output = np.genfromtxt(os.path.join('output','nn_periodic.out'), names=True,
                                    skip_header=1)
    np.testing.assert_allclose(corr2_output['r_nom'], dd.rnom, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['DD'], dd.npairs, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['npairs'], dd.npairs, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['RR'], rr.npairs, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xi'], xi, rtol=1.e-3)

    # If don't give a period, then an error.
    rr = treecorr.NNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins)
    with assert_raises(ValueError):
        rr.process(rcat1,rcat2, metric='Periodic')

    # Or if only give one kind of period
    rr = treecorr.NNCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, xperiod=3)
    with assert_raises(ValueError):

print('true_xi = ',true_xi)
    print('kk.xi = ',kk.xi)
    np.testing.assert_allclose(kk.xi, true_xi, rtol=1.e-4, atol=1.e-8)

    try:
        import fitsio
    except ImportError:
        print('Skipping FITS tests, since fitsio is not installed')
        return

    # Check that running via the corr2 script works correctly.
    config = treecorr.config.read_config('configs/kk_direct_spherical.yaml')
    cat1.write(config['file_name'])
    cat2.write(config['file_name2'])
    treecorr.corr2(config)
    data = fitsio.read(config['kk_file_name'])
    np.testing.assert_allclose(data['r_nom'], kk.rnom)
    np.testing.assert_allclose(data['npairs'], kk.npairs)
    np.testing.assert_allclose(data['weight'], kk.weight)
    np.testing.assert_allclose(data['xi'], kk.xi, rtol=1.e-3)

    # Repeat with binslop = 0
    # And don't do any top-level recursion so we actually test not going to the leaves.
    kk = treecorr.KKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins,
                                sep_units='deg', bin_slop=0, max_top=0)
    kk.process(cat1, cat2)
    np.testing.assert_array_equal(kk.npairs, true_npairs)
    np.testing.assert_allclose(kk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
    np.testing.assert_allclose(kk.xi, true_xi, rtol=1.e-3, atol=1.e-6)

print('true_xi = ',true_xi)
    print('kk.xi = ',kk.xi)
    np.testing.assert_allclose(kk.xi, true_xi, rtol=1.e-4, atol=1.e-8)

    try:
        import fitsio
    except ImportError:
        print('Skipping FITS tests, since fitsio is not installed')
        return

    # Check that running via the corr2 script works correctly.
    config = treecorr.config.read_config('configs/kk_direct.yaml')
    cat1.write(config['file_name'])
    cat2.write(config['file_name2'])
    treecorr.corr2(config)
    data = fitsio.read(config['kk_file_name'])
    np.testing.assert_allclose(data['r_nom'], kk.rnom)
    np.testing.assert_allclose(data['npairs'], kk.npairs)
    np.testing.assert_allclose(data['weight'], kk.weight)
    np.testing.assert_allclose(data['xi'], kk.xi, rtol=1.e-3)

    # Repeat with binslop = 0
    # And don't do any top-level recursion so we actually test not going to the leaves.
    kk = treecorr.KKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, bin_slop=0,
                                max_top=0)
    kk.process(cat1, cat2)
    np.testing.assert_array_equal(kk.npairs, true_npairs)
    np.testing.assert_allclose(kk.weight, true_weight, rtol=1.e-5, atol=1.e-8)
    np.testing.assert_allclose(kk.xi, true_xi, rtol=1.e-4, atol=1.e-8)

    # Check a few basic operations with a KKCorrelation object.

print('gg.npairs = ',gg.npairs)
    print('gg.xim = ',gg.xim)
    print('theory_gammat = ',theory_gQ)
    print('ratio = ',gg.xim / theory_gQ)
    print('diff = ',gg.xim - theory_gQ)
    print('max diff = ',max(abs(gg.xim - theory_gQ)))
    assert max(abs(gg.xim - theory_gQ)) < 4.e-5
    print('gg.xim_im = ',gg.xim_im)
    assert max(abs(gg.xim_im)) < 1.e-5

    # Check that we get the same result using the corr2 function:
    lens_cat.write(os.path.join('data','gg_oldrperp_lens.dat'))
    source_cat.write(os.path.join('data','gg_oldrperp_source.dat'))
    config = treecorr.read_config('configs/gg_oldrperp.yaml')
    config['verbose'] = 0
    treecorr.corr2(config)
    corr2_output = np.genfromtxt(os.path.join('output','gg_oldrperp.out'),names=True,
                                    skip_header=1)
    print('gg.xim = ',gg.xim)
    print('from corr2 output = ',corr2_output['xim'])
    print('ratio = ',corr2_output['xim']/gg.xim)
    print('diff = ',corr2_output['xim']-gg.xim)
    np.testing.assert_allclose(corr2_output['xim'], gg.xim, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xim_im'], gg.xim_im, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xip'], gg.xip, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xip_im'], gg.xip_im, rtol=1.e-3)

assert max(abs(gg.xip_im)) < 3.e-7 * tol_factor

    print('gg.xim = ',gg.xim)
    print('gg.xim_im = ',gg.xim_im)
    print('true_xim = ',true_xim)
    print('ratio = ',gg.xim / true_xim)
    print('diff = ',gg.xim - true_xim)
    print('max diff = ',max(abs(gg.xim - true_xim)))
    assert max(abs(gg.xim - true_xim)) < 2.e-7 * tol_factor
    assert max(abs(gg.xim_im)) < 2.e-7 * tol_factor

    # Check that we get the same result using the corr2 function
    cat.write(os.path.join('data','gg_spherical.dat'))
    config = treecorr.read_config('configs/gg_spherical.yaml')
    config['verbose'] = 0
    treecorr.corr2(config)
    corr2_output = np.genfromtxt(os.path.join('output','gg_spherical.out'), names=True,
                                    skip_header=1)
    print('gg.xip = ',gg.xip)
    print('from corr2 output = ',corr2_output['xip'])
    print('ratio = ',corr2_output['xip']/gg.xip)
    print('diff = ',corr2_output['xip']-gg.xip)
    np.testing.assert_allclose(corr2_output['xip'], gg.xip, rtol=1.e-3)

    print('gg.xim = ',gg.xim)
    print('from corr2 output = ',corr2_output['xim'])
    print('ratio = ',corr2_output['xim']/gg.xim)
    print('diff = ',corr2_output['xim']-gg.xim)
    np.testing.assert_allclose(corr2_output['xim'], gg.xim, rtol=1.e-3)

    print('xip_im from corr2 output = ',corr2_output['xip_im'])
    assert max(abs(corr2_output['xip_im'])) < 3.e-7 * tol_factor

corr2_output2 = np.genfromtxt(os.path.join('output','gg_m2.out'), names=True)
    print('mapsq = ',mapsq)
    print('from corr2 output = ',corr2_output2['Mapsq'])
    print('ratio = ',corr2_output2['Mapsq']/mapsq)
    print('diff = ',corr2_output2['Mapsq']-mapsq)
    np.testing.assert_allclose(corr2_output2['Mapsq'], mapsq, rtol=1.e-4)

    print('mxsq = ',mxsq)
    print('from corr2 output = ',corr2_output2['Mxsq'])
    print('ratio = ',corr2_output2['Mxsq']/mxsq)
    print('diff = ',corr2_output2['Mxsq']-mxsq)
    np.testing.assert_allclose(corr2_output2['Mxsq'], mxsq, rtol=1.e-4)

    # OK to have m2 output, but not gg
    del config['gg_file_name']
    treecorr.corr2(config)
    corr2_output2 = np.genfromtxt(os.path.join('output','gg_m2.out'), names=True)
    np.testing.assert_allclose(corr2_output2['Mapsq'], mapsq, rtol=1.e-4)
    np.testing.assert_allclose(corr2_output2['Mxsq'], mxsq, rtol=1.e-4)

    try:
        import fitsio
    except ImportError:
        print('Skipping FITS tests, since fitsio is not installed')
        return

    # Check the fits write option
    out_file_name = os.path.join('output','gg_out.fits')
    gg.write(out_file_name)
    data = fitsio.read(out_file_name)
    np.testing.assert_allclose(data['r_nom'], np.exp(gg.logr))
    np.testing.assert_allclose(data['meanr'], gg.meanr)

print('diff = ',kk.xi - true_xi)
    print('max diff = ',max(abs(kk.xi - true_xi)))
    print('max rel diff = ',max(abs((kk.xi - true_xi)/true_xi)))
    np.testing.assert_allclose(kk.xi, true_xi, rtol=0.1*tol_factor)

    # It should also work as a cross-correlation of this cat with itself
    kk.process(cat,cat)
    np.testing.assert_allclose(kk.meanlogr, np.log(kk.meanr), atol=1.e-3)
    np.testing.assert_allclose(kk.xi, true_xi, rtol=0.1*tol_factor)

    # Check that we get the same result using the corr2 function
    cat.write(os.path.join('data','kk.dat'))
    config = treecorr.read_config('configs/kk.yaml')
    config['verbose'] = 0
    config['precision'] = 8
    treecorr.corr2(config)
    corr2_output = np.genfromtxt(os.path.join('output','kk.out'), names=True, skip_header=1)
    print('kk.xi = ',kk.xi)
    print('from corr2 output = ',corr2_output['xi'])
    print('ratio = ',corr2_output['xi']/kk.xi)
    print('diff = ',corr2_output['xi']-kk.xi)
    np.testing.assert_allclose(corr2_output['xi'], kk.xi, rtol=1.e-3)

    try:
        import fitsio
    except ImportError:
        print('Skipping FITS tests, since fitsio is not installed')
        return

    # Check the fits write option
    out_file_name = os.path.join('output','kk_out.fits')
    kk.write(out_file_name)

print('gg.npairs = ',gg.npairs)
    print('gg.xim = ',gg.xim)
    print('theory_gammat = ',theory_gQ)
    print('ratio = ',gg.xim / theory_gQ)
    print('diff = ',gg.xim - theory_gQ)
    print('max diff = ',max(abs(gg.xim - theory_gQ)))
    assert max(abs(gg.xim - theory_gQ)) < 4.e-5
    print('gg.xim_im = ',gg.xim_im)
    assert max(abs(gg.xim_im)) < 1.e-5

    # Check that we get the same result using the corr2 function
    lens_cat.write(os.path.join('data','gg_rperp_lens.dat'))
    source_cat.write(os.path.join('data','gg_rperp_source.dat'))
    config = treecorr.read_config('configs/gg_rperp.yaml')
    config['verbose'] = 0
    treecorr.corr2(config)
    corr2_output = np.genfromtxt(os.path.join('output','gg_rperp.out'),names=True,
                                    skip_header=1)
    print('gg.xim = ',gg.xim)
    print('from corr2 output = ',corr2_output['xim'])
    print('ratio = ',corr2_output['xim']/gg.xim)
    print('diff = ',corr2_output['xim']-gg.xim)
    np.testing.assert_allclose(corr2_output['xim'], gg.xim, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xim_im'], gg.xim_im, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xip'], gg.xip, rtol=1.e-3)
    np.testing.assert_allclose(corr2_output['xip_im'], gg.xip_im, rtol=1.e-3)

treecorr.corr2(config)
    corr2_output = np.genfromtxt(os.path.join('output','nk_single.out'), names=True,
                                    skip_header=1)
    print('nk.xi = ',nk.xi)
    print('from corr2 output = ',corr2_output['kappa'])
    print('ratio = ',corr2_output['kappa']/nk.xi)
    print('diff = ',corr2_output['kappa']-nk.xi)
    np.testing.assert_allclose(corr2_output['kappa'], nk.xi, rtol=1.e-3)

    # There is special handling for single-row catalogs when using np.genfromtxt rather
    # than pandas.  So mock it up to make sure we test it.
    if sys.version_info < (3,): return  # mock only available on python 3
    from unittest import mock
    with mock.patch.dict(sys.modules, {'pandas':None}):
        with CaptureLog() as cl:
            treecorr.corr2(config, logger=cl.logger)
        assert "Unable to import pandas" in cl.output
    corr2_output = np.genfromtxt(os.path.join('output','nk_single.out'), names=True,
                                    skip_header=1)
    np.testing.assert_allclose(corr2_output['kappa'], nk.xi, rtol=1.e-3)