3x2pt Gaussian + SSC Covariance Matrix

3x2pt Gaussian + SSC covariance matrix to be used as input to CLOE for the MCMC runsThe Gauss+SSC covariance matrix is provided both in ASCII (.dat) and binary (.npy) format For comparison, the Gauss only case is also uploaded The covariance refers for the full data vector without any scale cuts so the total dimension is N x N with N = NoEll x [NoBinsWL (NoBinsWL + 1)/2 + NoBinsGC x NoBinsGC + NoBinsGC (NoBinsGC + 1)/2]with NoEll = number of multipoles = 32NoBinsWL = number of redshift bins for WL = 13NoBinsGC = number of redshift bins for GC = 13 thus giving N = 11232.本数据集为供CLOE开展马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）运行时作为输入使用的3×2点高斯+随机超样本协方差矩阵（Stochastic Super Sample Covariance, SSC）。 该高斯+SSC协方差矩阵同时提供了ASCII（.dat）与二进制（.npy）两种格式。 为便于对比，本次同时上传了仅含高斯项的协方差矩阵案例。 本协方差矩阵对应未经过任何尺度截断的完整数据矢量，其总维度为N×N，其中N的计算公式为： N = NoEll × [NoBinsWL × (NoBinsWL + 1)/2 + NoBinsGC × NoBinsGC + NoBinsGC × (NoBinsGC + 1)/2] 其中：多极数（number of multipoles, NoEll）= 32；弱引力透镜红移区间数（number of redshift bins for WL, NoBinsWL）= 13；星系成团红移区间数（number of redshift bins for GC, NoBinsGC）= 13。 由此可得N = 11232。