added Planck_compressed likelihood arXiv:1502.01590

montepython/likelihoods/Planck_compressed/Planck_compressed.data

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+# Covriance matrix of the compressed likelihood for Planck TT + lowP
+# (arXiv:1502.01590v2, Table 4)
+                                       # R, l_A, omega_b, n_s
+#Planck_compressed.correlation_matrix = [[1., 0.54, -0.63, -0.86],\ # R
+#                                        [0.54, 1., -0.43, -0.48],\ # l_A  
+#                                        [-0.63, -0.43, 1., 0.58],\ # omega_b
+#                                        [-0.86, -0.48, 0.58, 1.]] # n_s 
+
+# Covariance matrix_ij = sigma_i sigma_j Correlation_matrix_ij
+
+#Planck_compressed.inverse_correlation_matrix = np.linalg.inv([[1., 0.54, -0.63, -0.86], [0.54, 1., -0.43, -0.48],  [-0.63, -0.43, 1., 0.58], [-0.86, -0.48, 0.58, 1.]]) 
+
+Planck_compressed.inverse_correlation_matrix = [[ 4.51266537, -0.59697118, 0.75581806,  3.15597158], [-0.59697118,  1.43957767,  0.21084593,  0.05531143], [ 0.75581806,  0.21084593,  1.70453221, -0.2374191 ], [ 3.15597158, 0.05531143, -0.2374191 ,  3.87838813]]
+
+                      # mean, std. dev. (sigma)
+#Planck_compressed.R = [1.7488, 0.0074]
+#Planck_compressed.l_a = [301.76, 0.14]
+#Planck_compressed.omega_b = [0.02228, 0.00023]
+#Planck_compressed.n_s = [0.9660, 0.0061]
+
+#Planck_compressed.planck_values = np.array([[1.7488, 0.0074], [301.76, 0.14], [0.02228, 0.00023], [0.9660, 0.0061]])
+Planck_compressed.planck_values = [[1.7488, 0.0074], [301.76, 0.14], [0.02228, 0.00023], [0.9660, 0.0061]]
+
+

montepython/likelihoods/Planck_compressed/__init__.py

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+from montepython.likelihood_class import Likelihood_prior
+import numpy as np
+
+
+class Planck_compressed(Likelihood_prior):
+
+    # initialisation of the class is done within the parent Likelihood_prior. For
+    # this case, it does not differ, actually, from the __init__ method in
+    # Likelihood class.
+
+    def loglkl(self, cosmo, data):
+
+        z_star = cosmo.z_optical_depth_unity()  # z at which the optical depth is unity
+        D_A_star = cosmo.angular_distance(z_star)
+        rs_star = cosmo.rs_at_z(z_star)
+
+        R = np.sqrt(cosmo.Omega_m()) * cosmo.Hubble(0) * D_A_star
+        l_a = np.pi * D_A_star / rs_star
+        omega_b = cosmo.omega_b()
+        n_s = cosmo.n_s()
+
+        values = np.array([R, l_a, omega_b, n_s])
+        planck_values = zip(*self.planck_values)
+
+        # loglkl = -1/2 * (x_i - x_mean_i) * 1/sigma_i * corr_mat_ij^-1 *
+        #                 1/sigma_j * (x_j - x_mean_j)
+
+        differences_over_sigma = ((values[:][0] - planck_values[0]) /
+                                  planck_values[1])
+
+        loglkl = -0.5 * np.dot(differences_over_sigma,
+                               np.dot(self.inverse_correlation_matrix,
+                                      differences_over_sigma))
+        return loglkl