Synthetic Pell and cov for GCspectro

Includes binned power spectrum multipoles and Gaussian covariance, to be used with cloelib/cloelike. Produced up to kmax = 0.4 h/Mpc with 100 linearly spaced bins (using the publicly available COMET code). Uses 4 redshift bins, from z=0.9 to z=1.8, with width of Delta_z=(0.2, 0.2, 0.2, 0.3) Both the synthetic data and the covariance were generated with the following parameters:parameters = { 'H0': 67.0, 'Omega_cdm0': 0.27, 'Omega_b0': 0.049, 'Omega_k0': 0.0, 'mnu': 0.0, 'w0': -1.0, 'wa': 0.0, 'ns': 0.96, 'As': 2.1e-9, 'gamma_MG': 0.545, 'b1': np.array([1.412, 1.769, 2.039, 2.496]), 'b2': np.array([0.695, 0.870, 1.162, 2.010]), 'bG2': np.array([-0.156, -0.299, -0.400, -0.555]), 'bGam3': np.array([0.323, 0.621, 0.827, 1.137]), 'c0': np.array([30.948, 37.116, 36.738, 53.627]), (Mpc^2 units) 'c2': np.array([46.233, 53.071, 48.626, 60.962]), (Mpc^2 units) 'c4': np.array([10.057, 10.385, 8.643, 8.711]), (Mpc^2 units) 'cnlo': np.array([0.0, 0.0, 0.0, 0.0]), 'NP0': np.array([1.056, 1.152, 1.144, 1.309]), 'NP20': np.array([0.0, 0.0, 0.0, 0.0]), 'NP22': np.array([0.0, 0.0, 0.0, 0.0]), 'fout': np.array([0.0, 0.0, 0.0, 0.0]), 'sigmaz': np.array([0.0, 0.0, 0.0, 0.0])}本数据集包含分箱功率谱多极矩（power spectrum multipoles）与高斯协方差（Gaussian covariance），可与cloelib/cloelike配套使用。该数据集采用公开可用的COMET代码生成，最大波数kmax设置为0.4 h/Mpc，共包含100个线性均匀分布的波数箱。 本数据集使用4个红移区间，红移范围为z=0.9至z=1.8，各区间宽度依次为Δz=(0.2, 0.2, 0.2, 0.3)。 合成数据与协方差矩阵均基于以下参数生成： parameters = { 'H0': 67.0, 'Omega_cdm0': 0.27, 'Omega_b0': 0.049, 'Omega_k0': 0.0, 'mnu': 0.0, 'w0': -1.0, 'wa': 0.0, 'ns': 0.96, 'As': 2.1e-9, 'gamma_MG': 0.545, 'b1': np.array([1.412, 1.769, 2.039, 2.496]), 'b2': np.array([0.695, 0.870, 1.162, 2.010]), 'bG2': np.array([-0.156, -0.299, -0.400, -0.555]), 'bGam3': np.array([0.323, 0.621, 0.827, 1.137]), 'c0': np.array([30.948, 37.116, 36.738, 53.627]), # 单位为Mpc² 'c2': np.array([46.233, 53.071, 48.626, 60.962]), # 单位为Mpc² 'c4': np.array([10.057, 10.385, 8.643, 8.711]), # 单位为Mpc² 'cnlo': np.array([0.0, 0.0, 0.0, 0.0]), 'NP0': np.array([1.056, 1.152, 1.144, 1.309]), 'NP20': np.array([0.0, 0.0, 0.0, 0.0]), 'NP22': np.array([0.0, 0.0, 0.0, 0.0]), 'fout': np.array([0.0, 0.0, 0.0, 0.0]), 'sigmaz': np.array([0.0, 0.0, 0.0, 0.0]) }