v2.0.2 data for MCMC runs

<p>Input files, data vectors and covariance matrices for the MCMC runs with CLOE v2.0.2&nbsp;<br><br>- <em>nzTabSPV3.dat:</em> n(z) for the 13 equipopulated redshift bins case<br>- <em>nuiTabSPV3.dat:</em> fiducial values of the nuisance parameters (#bin centre, source number density, galaxy bias, logaritmic slope of the luminosity function, shift in bin centre, variance of the zp - z relation)<br>&nbsp;</p> <p>- <em>Cls_zNLA_PosPos_C00.dat:</em> GCph data vector<br>- <em>Cls_zNLA_PosShear_C00.dat:</em> GGL data vector (in the order GL)<br>- <em>Cls_zNLA_ShearShear_C00.dat:</em> WL data vector<br>&nbsp;</p> <p>- <em>CovMat-3x2pt-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix</p> <p>- <em>CovMat-3x2pt-BNT-Gauss-32Bins.npy:</em> Gauss only 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>CovMat-3x2pt-BNT-GaussSSC-32Bins.npy:</em> Gauss + SSC 3x2pt covariance matrix, BNT-transformed</p> <p>- <em>BNT-matrix.npy</em>: BNT matrix used for transforming the covariance matrices</p> <p><br>- <em>power_galaxies_EFT_benchmark_z1..fits:</em> GCsp data vector for bin centred on z = 1.0</p> <p>- <em>power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp data vector for bin centred on z = 1.2</p> <p>- <em>power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp data vector for bin centred on z = 1.4</p> <p>- <em>power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp data vector for bin centred on z = 1.65</p> <p>&nbsp;</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1..fits:</em> GCsp Gauss only covariance for bin centred on z = 1.0</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.2.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.2</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.4.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.4</p> <p>- <em>cov_power_galaxies_EFT_benchmark_z1.65.fits:</em> GCsp Gauss only covariance for bin centred on z = 1.65</p>