Planck PR4: Lollipop and Hillipop

covmat/planck20_TTTEEE.covmat

@@ -0,0 +1,28 @@
+#          omega_b,        omega_cdm,              n_s,     ln10^{10}A_s,         tau_reio,      100*theta_s,         A_planck,          cal100A,          cal100B,          cal143B,          cal217A,          cal217B,           AdustT,       beta_dustT,           AdustP,       beta_dustP,           Aradio,           Adusty,       beta_dusty,             Acib,             Atsz,             Aksz,               xi,           z_reio,     Omega_Lambda,              A_s,               H0
+ 1.840240e-08	-9.844223e-08	 2.461074e-07	 3.466029e-07	 1.791125e-07	 9.747048e-09	 2.114019e-08	-7.859908e-08	-1.849875e-08	-9.439712e-08	-3.123796e-08	-5.127669e-08	-1.218057e-07	 1.612356e-08	 2.057271e-07	-4.992869e-08	 9.731289e-07	 9.862855e-07	 4.055256e-08	 3.925337e-07	 3.954476e-06	-7.070384e-05	 7.713336e-07	 1.197509e-05	 6.777182e-07	 7.235976e-16	 5.478000e-05	
+-9.844223e-08	 1.453346e-06	-3.458809e-06	-1.418125e-06	-1.803109e-06	-9.490650e-08	-1.898047e-07	 2.862719e-07	 5.488950e-07	 8.259054e-07	 2.585908e-07	 4.928197e-07	 6.632473e-07	-2.328195e-08	-1.609441e-06	 9.665055e-07	 9.515673e-06	-4.229444e-06	 1.817085e-07	-3.786795e-06	-3.373463e-05	 2.469045e-04	-5.891204e-06	-1.335299e-04	-8.820154e-06	-2.956279e-15	-6.377312e-04	
+ 2.461074e-07	-3.458809e-06	 1.616490e-05	 7.233634e-06	 6.445059e-06	 3.050385e-07	 5.077061e-07	-1.275205e-06	-1.039523e-06	-2.432890e-06	-1.023703e-06	-1.615055e-06	 1.016594e-05	-2.799777e-07	 9.091211e-06	-3.926106e-06	 5.673456e-04	 2.354717e-04	-3.976805e-06	-9.521328e-05	-1.984541e-04	-3.639022e-03	 3.056258e-05	 5.324632e-04	 2.129765e-05	 1.507474e-14	 1.555086e-03	
+ 3.466029e-07	-1.418125e-06	 7.233634e-06	 1.919708e-04	 6.987095e-05	 2.742058e-07	 9.730918e-06	-1.859432e-05	-1.410698e-05	-3.246388e-05	-1.721372e-05	-1.444148e-05	 1.179817e-05	 7.041550e-07	 2.733993e-05	-2.511366e-06	 2.764393e-03	 4.339771e-04	 6.333277e-06	 1.100864e-04	 5.022837e-04	-4.753779e-03	-4.535069e-05	 6.906325e-03	 1.063569e-05	 4.011037e-13	 9.077986e-04	
+ 1.791125e-07	-1.803109e-06	 6.445059e-06	 6.987095e-05	 3.828166e-05	 1.337671e-07	-7.752258e-07	-3.092030e-07	 1.333381e-06	 1.047667e-06	 7.361560e-07	 6.099767e-07	-9.288704e-06	-1.624189e-07	-1.731937e-06	-2.054725e-06	-4.848227e-04	-2.691597e-05	 3.661810e-07	 4.066851e-05	 4.333970e-05	-1.571131e-03	-1.453730e-05	 3.767535e-03	 1.131767e-05	 1.460042e-13	 8.480720e-04	
+ 9.747048e-09	-9.490650e-08	 3.050385e-07	 2.742058e-07	 1.337671e-07	 6.397486e-08	 3.985364e-08	-5.890412e-08	-4.509948e-08	-1.026150e-07	-4.771522e-08	-4.657357e-08	 1.037112e-07	 4.201974e-08	 1.065874e-07	-6.603654e-08	 1.943370e-06	 1.955609e-06	 1.116957e-07	-8.019492e-07	 7.874163e-06	-1.031093e-05	-1.660969e-07	 9.697638e-06	 7.736771e-07	 5.720486e-16	 6.389753e-05	
+ 2.114019e-08	-1.898047e-07	 5.077061e-07	 9.730918e-06	-7.752258e-07	 3.985364e-08	 6.030081e-06	 6.388278e-07	-2.181853e-07	 4.407630e-07	 4.953891e-08	 4.628820e-07	 6.127432e-06	-1.107084e-07	 6.724249e-06	-7.171283e-07	 8.468127e-04	 9.146022e-05	-9.103157e-07	 9.454978e-06	 1.026862e-04	-1.819521e-04	-5.557621e-06	-8.530258e-05	 1.287541e-06	 2.032108e-14	 9.977298e-05	
+-7.859908e-08	 2.862719e-07	-1.275205e-06	-1.859432e-05	-3.092030e-07	-5.890412e-08	 6.388278e-07	 4.597547e-05	-2.618571e-05	 1.925199e-05	 9.532142e-06	 9.845116e-06	-4.421378e-06	-2.479901e-07	-1.425274e-05	-2.403668e-06	-4.461737e-03	 6.863017e-05	-1.957729e-06	-9.980441e-05	 1.155702e-03	 4.176499e-04	-1.773587e-04	-9.550186e-06	-2.195769e-06	-3.884571e-14	-1.927229e-04	
+-1.849875e-08	 5.488950e-07	-1.039523e-06	-1.410698e-05	 1.333381e-06	-4.509948e-08	-2.181853e-07	-2.618571e-05	 4.377068e-05	 1.781603e-05	 9.553313e-06	 7.558668e-06	-6.551631e-06	-5.723337e-07	-9.084896e-06	 2.198280e-06	 3.131913e-03	-3.367207e-04	-5.261447e-06	 6.180468e-05	-1.802217e-03	 2.431963e-04	 1.894150e-04	 1.470479e-04	-3.264674e-06	-2.946301e-14	-2.266320e-04	
+-9.439712e-08	 8.259054e-07	-2.432890e-06	-3.246388e-05	 1.047667e-06	-1.026150e-07	 4.407630e-07	 1.925199e-05	 1.781603e-05	 3.700643e-05	 1.916173e-05	 1.756032e-05	-1.654469e-05	-5.922455e-07	-2.328865e-05	-1.186339e-07	-1.032480e-03	-2.586462e-04	-5.832466e-06	-1.270953e-05	-5.151174e-04	 5.053475e-04	-1.318968e-05	 1.389484e-04	-5.390549e-06	-6.781155e-14	-4.131433e-04	
+-3.123796e-08	 2.585908e-07	-1.023703e-06	-1.721372e-05	 7.361560e-07	-4.771522e-08	 4.953891e-08	 9.532142e-06	 9.553313e-06	 1.916173e-05	 5.809793e-05	-3.814880e-05	-1.627697e-05	-1.229002e-06	-1.246862e-05	 4.233637e-07	-1.279889e-03	 6.936332e-05	-3.144755e-06	-9.650052e-05	 2.404845e-04	 2.799154e-04	 2.463971e-05	 8.347604e-05	-1.749373e-06	-3.594227e-14	-1.360365e-04	
+-5.127669e-08	 4.928197e-07	-1.615055e-06	-1.444148e-05	 6.099767e-07	-4.657357e-08	 4.628820e-07	 9.845116e-06	 7.558668e-06	 1.756032e-05	-3.814880e-05	 5.669923e-05	-1.949274e-05	 4.645827e-07	-1.129255e-05	-7.126504e-07	-1.708754e-03	-3.966261e-05	-6.495942e-06	 1.280121e-05	 4.693752e-04	 3.353928e-04	-1.058132e-04	 8.130375e-05	-3.136340e-06	-3.016777e-14	-2.372048e-04	
+-1.218057e-07	 6.632473e-07	 1.016594e-05	 1.179817e-05	-9.288704e-06	 1.037112e-07	 6.127432e-06	-4.421378e-06	-6.551631e-06	-1.654469e-05	-1.627697e-05	-1.949274e-05	 7.774506e-04	 4.581974e-05	-1.253732e-05	-1.016558e-05	 1.225531e-03	 6.226687e-03	-4.098436e-05	-6.874444e-03	-5.877500e-03	-6.792076e-03	 2.005832e-03	-8.869159e-04	-4.005608e-06	 2.452478e-14	-3.115967e-04	
+ 1.612356e-08	-2.328195e-08	-2.799777e-07	 7.041550e-07	-1.624189e-07	 4.201974e-08	-1.107084e-07	-2.479901e-07	-5.723337e-07	-5.922455e-07	-1.229002e-06	 4.645827e-07	 4.581974e-05	 9.979283e-05	 1.198632e-06	-9.581335e-07	 4.797010e-04	 5.994743e-05	-7.095976e-06	 1.712167e-05	-3.669122e-04	 2.183215e-04	-4.650691e-05	-1.781926e-05	 3.777093e-07	 1.473960e-15	 3.584764e-05	
+ 2.057271e-07	-1.609441e-06	 9.091211e-06	 2.733993e-05	-1.731937e-06	 1.065874e-07	 6.724249e-06	-1.425274e-05	-9.084896e-06	-2.328865e-05	-1.246862e-05	-1.129255e-05	-1.253732e-05	 1.198632e-06	 6.763898e-04	 3.218554e-04	 5.397361e-03	-5.429908e-04	 7.773019e-06	 5.785127e-04	-1.002273e-03	-9.984173e-04	 2.619928e-05	-2.414659e-04	 1.033572e-05	 5.698178e-14	 7.944932e-04	
+-4.992869e-08	 9.665055e-07	-3.926106e-06	-2.511366e-06	-2.054725e-06	-6.603654e-08	-7.171283e-07	-2.403668e-06	 2.198280e-06	-1.186339e-07	 4.233637e-07	-7.126504e-07	-1.016558e-05	-9.581335e-07	 3.218554e-04	 3.414193e-04	 1.145459e-03	-1.974027e-04	 5.635922e-06	 1.582577e-04	-2.080866e-04	 3.064776e-04	-4.739990e-05	-1.752200e-04	-5.769069e-06	-5.232584e-15	-4.114280e-04	
+ 9.731289e-07	 9.515673e-06	 5.673456e-04	 2.764393e-03	-4.848227e-04	 1.943370e-06	 8.468127e-04	-4.461737e-03	 3.131913e-03	-1.032480e-03	-1.279889e-03	-1.708754e-03	 1.225531e-03	 4.797010e-04	 5.397361e-03	 1.145459e-03	 2.251021e+01	-8.509432e-01	 2.326935e-02	 3.380926e-01	-5.897223e+00	-2.219705e+00	 5.642074e-01	-4.911396e-02	-4.732883e-05	 5.758056e-12	-1.588059e-03	
+ 9.862855e-07	-4.229444e-06	 2.354717e-04	 4.339771e-04	-2.691597e-05	 1.955609e-06	 9.146022e-05	 6.863017e-05	-3.367207e-04	-2.586462e-04	 6.936332e-05	-3.966261e-05	 6.226687e-03	 5.994743e-05	-5.429908e-04	-1.974027e-04	-8.509432e-01	 3.490880e-01	-1.409323e-02	-1.328494e-01	 4.654434e-01	-4.728364e-01	-1.808381e-02	-2.976879e-03	 3.654253e-05	 9.071442e-13	 2.991911e-03	
+ 4.055256e-08	 1.817085e-07	-3.976805e-06	 6.333277e-06	 3.661810e-07	 1.116957e-07	-9.103157e-07	-1.957729e-06	-5.261447e-06	-5.832466e-06	-3.144755e-06	-6.495942e-06	-4.098436e-05	-7.095976e-06	 7.773019e-06	 5.635922e-06	 2.326935e-02	-1.409323e-02	 2.006805e-03	-6.223253e-04	-7.418521e-03	 2.562491e-03	-1.117960e-03	 3.492301e-05	-5.427980e-07	 1.311581e-14	 1.306783e-05	
+ 3.925337e-07	-3.786795e-06	-9.521328e-05	 1.100864e-04	 4.066851e-05	-8.019492e-07	 9.454978e-06	-9.980441e-05	 6.180468e-05	-1.270953e-05	-9.650052e-05	 1.280121e-05	-6.874444e-03	 1.712167e-05	 5.785127e-04	 1.582577e-04	 3.380926e-01	-1.328494e-01	-6.223253e-04	 1.205578e-01	-8.948235e-02	 7.040759e-03	-3.102271e-02	 3.874726e-03	 2.054708e-05	 2.302574e-13	 1.434847e-03	
+ 3.954476e-06	-3.373463e-05	-1.984541e-04	 5.022837e-04	 4.333970e-05	 7.874163e-06	 1.026862e-04	 1.155702e-03	-1.802217e-03	-5.151174e-04	 2.404845e-04	 4.693752e-04	-5.877500e-03	-3.669122e-04	-1.002273e-03	-2.080866e-04	-5.897223e+00	 4.654434e-01	-7.418521e-03	-8.948235e-02	 2.773842e+00	-7.441553e-01	-3.626788e-01	 2.712531e-03	 2.347130e-04	 1.060807e-12	 1.808377e-02	
+-7.070384e-05	 2.469045e-04	-3.639022e-03	-4.753779e-03	-1.571131e-03	-1.031093e-05	-1.819521e-04	 4.176499e-04	 2.431963e-04	 5.053475e-04	 2.799154e-04	 3.353928e-04	-6.792076e-03	 2.183215e-04	-9.984173e-04	 3.064776e-04	-2.219705e+00	-4.728364e-01	 2.562491e-03	 7.040759e-03	-7.441553e-01	 5.556855e+00	 3.364099e-01	-1.369179e-01	-1.803093e-03	-9.916522e-12	-1.546286e-01	
+ 7.713336e-07	-5.891204e-06	 3.056258e-05	-4.535069e-05	-1.453730e-05	-1.660969e-07	-5.557621e-06	-1.773587e-04	 1.894150e-04	-1.318968e-05	 2.463971e-05	-1.058132e-04	 2.005832e-03	-4.650691e-05	 2.619928e-05	-4.739990e-05	 5.642074e-01	-1.808381e-02	-1.117960e-03	-3.102271e-02	-3.626788e-01	 3.364099e-01	 9.293302e-02	-1.664305e-03	 3.565081e-05	-9.577948e-14	 2.756848e-03	
+ 1.197509e-05	-1.335299e-04	 5.324632e-04	 6.906325e-03	 3.767535e-03	 9.697638e-06	-8.530258e-05	-9.550186e-06	 1.470479e-04	 1.389484e-04	 8.347604e-05	 8.130375e-05	-8.869159e-04	-1.781926e-05	-2.414659e-04	-1.752200e-04	-4.911396e-02	-2.976879e-03	 3.492301e-05	 3.874726e-03	 2.712531e-03	-1.369179e-01	-1.664305e-03	 3.731198e-01	 8.296338e-04	 1.442919e-11	 6.159178e-02	
+ 6.777182e-07	-8.820154e-06	 2.129765e-05	 1.063569e-05	 1.131767e-05	 7.736771e-07	 1.287541e-06	-2.195769e-06	-3.264674e-06	-5.390549e-06	-1.749373e-06	-3.136340e-06	-4.005608e-06	 3.777093e-07	 1.033572e-05	-5.769069e-06	-4.732883e-05	 3.654253e-05	-5.427980e-07	 2.054708e-05	 2.347130e-04	-1.803093e-03	 3.565081e-05	 8.296338e-04	 5.462667e-05	 2.217831e-14	 4.007586e-03	
+ 7.235976e-16	-2.956279e-15	 1.507474e-14	 4.011037e-13	 1.460042e-13	 5.720486e-16	 2.032108e-14	-3.884571e-14	-2.946301e-14	-6.781155e-14	-3.594227e-14	-3.016777e-14	 2.452478e-14	 1.473960e-15	 5.698178e-14	-5.232584e-15	 5.758056e-12	 9.071442e-13	 1.311581e-14	 2.302574e-13	 1.060807e-12	-9.916522e-12	-9.577948e-14	 1.442919e-11	 2.217831e-14	 8.381452e-22	 1.893555e-12	
+ 5.478000e-05	-6.377312e-04	 1.555086e-03	 9.077986e-04	 8.480720e-04	 6.389753e-05	 9.977298e-05	-1.927229e-04	-2.266320e-04	-4.131433e-04	-1.360365e-04	-2.372048e-04	-3.115967e-04	 3.584764e-05	 7.944932e-04	-4.114280e-04	-1.588059e-03	 2.991911e-03	 1.306783e-05	 1.434847e-03	 1.808377e-02	-1.546286e-01	 2.756848e-03	 6.159178e-02	 4.007586e-03	 1.893555e-12	 2.976239e-01	

input/Planck20_TTTEEE.param

@@ -0,0 +1,102 @@
+#------Experiments to test (separated with commas)-----
+# To see all the available ones, please look in the
+# montepython/likelihoods folder. Every folder there is a valid likelihood to
+# test.
+data.experiments=['Planck20_Hillipop_TTTEEE','Planck_lowl_TT','Planck20_Lollipop_EE']
+
+
+#------ Settings for the over-sampling.
+# The first element will always be set to 1, for it is the sampling of the
+# cosmological parameters. The other numbers describe the over sampling of the
+# nuisance parameter space. This array must have the same dimension as the
+# number of blocks in your run (so, 1 for cosmological parameters, and then 1
+# for each experiment with varying nuisance parameters).
+# Note that when using Planck likelihoods, you definitely want to use [1, 4],
+# to oversample as much as possible the 14 nuisance parameters.
+# Remember to order manually the experiments from slowest to fastest (putting
+# Planck as the first set of experiments should be a safe bet, except if you
+# also have LSS experiments).
+# If you have experiments without nuisance, you do not need to specify an
+# additional entry in the over_sampling list (notice for instance that, out of
+# the three Planck likelihoods used, only Planck_highl requires nuisance
+# parameters, therefore over_sampling has a length of two (cosmology, plus one
+# set of nuisance).
+data.over_sampling=[1, 4]
+
+
+
+#------ Parameter list -------
+# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
+# - if min max irrelevant, put to -1 or None (if you want a boundary of -1, use -1.0)
+# - if fixed, put 1-sigma to 0
+# - if scale irrelevant, put to 1, otherwise to the appropriate factor
+# - role is either 'cosmo', 'nuisance' or 'derived'
+
+
+# Cosmological parameters list
+data.parameters['omega_b'] 	= [0.0223, 0.017, 0.027, 0.0002,  1, 'cosmo']
+data.parameters['omega_cdm'] 	= [0.1188,  0.09,  0.15,  0.002,  1, 'cosmo']
+data.parameters['n_s']       	= [ 0.968,   0.9,   1.1,  0.005,  1, 'cosmo']
+data.parameters['ln10^{10}A_s'] = [ 3.040,   2.7,   3.4,   0.02,  1, 'cosmo']
+data.parameters['tau_reio']    	= [ 0.058,  0.01,  0.17,  0.008,  1, 'cosmo']
+data.parameters['100*theta_s']  = [1.0410,  1.03,  1.05, 0.0003,  1, 'cosmo']
+
+# Nuisance parameter list, same call, except the name does not have to be a class name
+# Note the nuisance parameters must follow *immediately after* the cosmo parameters,
+# MP expects cosmo, nuisance, derived
+
+# Nuisance parameters
+data.parameters['A_planck']    = [  1.0,  0.9,  1.1, 0.0025, 1, 'nuisance']
+data.parameters['cal100A']     = [  1.0,  0.9,  1.1,  0.005, 1, 'nuisance']
+data.parameters['cal100B']     = [  1.0,  0.9,  1.1,  0.005, 1, 'nuisance']
+data.parameters['cal143A']     = [  1.0,    1,    1,      0, 1, 'nuisance']
+data.parameters['cal143B']     = [  1.0,  0.9,  1.1,  0.005, 1, 'nuisance']
+data.parameters['cal217A']     = [  1.0,  0.9,  1.1,  0.005, 1, 'nuisance']
+data.parameters['cal217B']     = [  1.0,  0.9,  1.1,  0.005, 1, 'nuisance']
+
+data.parameters['AdustT']      = [  1.0,  0.5,  1.5,   0.03, 1, 'nuisance']
+data.parameters['beta_dustT']  = [ 1.51,  1.2,  1.8,   0.01, 1, 'nuisance']
+data.parameters['AdustP']      = [  1.0,  0.5,  1.5,   0.03, 1, 'nuisance']
+data.parameters['beta_dustP']  = [ 1.58,  1.2,  1.8,   0.02, 1, 'nuisance']
+data.parameters['Aradio']      = [ 63.3,    0,  150,    4.7, 1, 'nuisance']
+data.parameters['beta_radio']  = [ -0.8, -0.8, -0.8,      0, 1, 'nuisance']
+data.parameters['Adusty']      = [ 6.07,    0,  100,    0.6, 1, 'nuisance']
+data.parameters['beta_dusty']  = [ 1.75,  1.6,  1.9,   0.06, 1, 'nuisance']
+data.parameters['Acib']        = [  1.0,   0.,   20,    0.3, 1, 'nuisance']
+data.parameters['Atsz']        = [  5.9,   0.,   50,    1.6, 1, 'nuisance']
+data.parameters['Aksz']        = [  1.0,   0.,   50,    2.5, 1, 'nuisance']
+data.parameters['xi']          = [  0.4,  -1.,   1.,    0.3, 1, 'nuisance']
+
+# Derived parameter list
+data.parameters['z_reio']       = [0, None, None, 0,    1, 'derived']
+data.parameters['Omega_Lambda'] = [0, None, None, 0,    1, 'derived']
+data.parameters['A_s']          = [0, None, None, 0, 1e-9, 'derived']
+data.parameters['H0']           = [0, None, None, 0,    1, 'derived']
+#data.parameters['sigma8']       = [0, None, None, 0,    1, 'derived']
+
+
+# CLASS parameters
+data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/external/bbn/sBBN_2017.dat'
+data.cosmo_arguments['k_pivot'] = 0.05
+data.cosmo_arguments['N_ur'] = 2.0328
+data.cosmo_arguments['N_ncdm'] = 1
+data.cosmo_arguments['m_ncdm'] = 0.06
+data.cosmo_arguments['T_ncdm'] = 0.71611
+data.cosmo_arguments['l_max_scalars'] = 2500
+data.cosmo_arguments['non_linear'] = 'halofit'
+
+# These two are required to get sigma8 as a derived parameter
+# (class must compute the P(k) until sufficient k)
+#data.cosmo_arguments['output'] = 'mPk'
+#data.cosmo_arguments['P_k_max_h/Mpc'] = 1.
+
+
+#------ Mcmc parameters ----
+# Number of steps taken, by default (overwritten by the -N command)
+data.N=10
+# Number of accepted steps before writing to file the chain. Larger means less
+# access to disc, but this is not so much time consuming.
+data.write_step=5
+
+
+# Note: MP will only interpret comments if the line begins with # (you cannot add comments at the end of lines!)

montepython/likelihoods/Planck20_Hillipop_TT/Planck20_Hillipop_TT.data

@@ -0,0 +1,13 @@
+Planck20_Hillipop_TT.data_directory = ''
+Planck20_Hillipop_TT.file           = ''
+
+Planck20_Hillipop_TT.use_nuisance = ['A_planck','cal100A','cal100B','cal143A','cal143B','cal217A','cal217B','Aradio','Adusty','AdustT','Acib','Atsz','Aksz','Aksz','xi','beta_dustT','beta_radio','beta_dusty']
+
+Planck20_Hillipop_TT.A_planck_prior_center = 1
+Planck20_Hillipop_TT.A_planck_prior_std = 0.0025
+Planck20_Hillipop_TT.AdustT_prior_center = 1.0
+Planck20_Hillipop_TT.AdustT_prior_std = 0.1
+Planck20_Hillipop_TT.beta_dustT_prior_center = 1.51
+Planck20_Hillipop_TT.beta_dustT_prior_std = 0.01
+Planck20_Hillipop_TT.beta_dusty_prior_center = 1.75
+Planck20_Hillipop_TT.beta_dusty_prior_std = 0.06

montepython/likelihoods/Planck20_Hillipop_TT/Planck20_Hillipop_TT.py

@@ -0,0 +1,62 @@
+from montepython.likelihood_class import Likelihood
+import numpy as np
+import planck_2020_hillipop
+import os
+import tempfile
+
+packages_path = os.environ.get("COBAYA_PACKAGES_PATH") or os.path.join(
+    tempfile.gettempdir(), "Hillipop_packages"
+)
+
+class Planck20_Hillipop_TT(Likelihood):
+    def __init__(self, path, data, command_line):
+        Likelihood.__init__(self, path, data, command_line)
+
+        #Create Cobaya likelihood
+        self.lik = planck_2020_hillipop.TT({"packages_path": packages_path})
+
+        self.need_cosmo_arguments(
+            data, {'lensing': 'yes', 'output': 'tCl pCl lCl', 'l_max_scalars': self.lik.lmax})
+
+        print( "Init Hillipop TT done !")
+
+    def loglkl(self, cosmo, data):
+
+        cls = self.get_cl(cosmo)
+
+        fac = cls['ell'] * (cls['ell']+1) / (2*np.pi)
+        dl = {mode:np.zeros(self.lik.lmax+1) for mode in ['TT','TE','EE']}
+        for mode in ['TT','TE','EE']:
+            dl[mode][cls['ell']] = fac*cls[mode.lower()]
+
+        data_params = {par:data.mcmc_parameters[par]['current'] for par in data.get_mcmc_parameters(['nuisance'])}
+
+        #fix beta_cib to beta_dusty
+        if 'beta_cib' not in data_params:
+            data_params['beta_cib'] = data_params['beta_dusty']
+
+        #compute log-likelihood
+        lkl = self.lik.loglike(dl, **data_params)
+#        print( lkl)
+
+        #Add priors
+        lkl = self.add_nuisance_prior(lkl, data)
+
+        return lkl
+
+
+    def add_nuisance_prior(self, lkl, data):
+        # Recover the current value of the nuisance parameter.
+        for nuisance in self.use_nuisance:
+            nuisance_value = float(
+                data.mcmc_parameters[nuisance]['current'] *
+                data.mcmc_parameters[nuisance]['scale'])
+
+            # add prior on nuisance parameters
+            if hasattr(self, "%s_prior_center" % nuisance) and getattr(self, "%s_prior_std" % nuisance) > 0:
+                # convenience variables
+                prior_center = getattr(self, "%s_prior_center" % nuisance)
+                prior_std = getattr(self, "%s_prior_std" % nuisance)
+                lkl += -0.5*((nuisance_value-prior_center)/prior_std)**2
+
+        return lkl

montepython/likelihoods/Planck20_Hillipop_TT/README.md

@@ -0,0 +1,22 @@
+# Hillipop is a high-l polarized likelihood for Planck PR4 data. 
+
+Hillipop is a multifrequency CMB likelihood for Planck data. The likelihood is a spectrum-based Gaussian approximation for cross-correlation spectra from Planck 100, 143 and 217GHz split-frequency maps, with semi-analytic estimates of the Cl covariance matrix based on the data. The cross-spectra are debiased from the effects of the mask and the beam leakage using Xpol (a generalization to polarization of the algorithm presented in Tristram et al. 2005) before being compared to the model, which includes CMB and foreground residuals. They cover the multipoles from ℓ = 30 to 2500.
+
+Reference:\
+[Tristram et al., A&A, 2023](https://arxiv.org/abs/2309.10034)
+
+The code is available here:\
+[https://github.com/planck-npipe/hillipop](https://github.com/planck-npipe/hillipop)\
+This is a wrapper for MontePython.
+
+You need to install the code before:
+```
+pip install planck-2020-hillipop
+```
+
+Then get the data, untar and set the variable $COBAYA_PACKAGES_PATH to the local directory:
+```
+wget https://portal.nersc.gov/cfs/cmb/planck2020/likelihoods/planck_2020_hillipop_TT_v4.2.tar.gz
+tar -zxvf planck_2020_hillipop_TT_v4.2.tar.gz --directory /path/to/data
+export COBAYA_PACKAGES_PATH=/path/to/data
+```

montepython/likelihoods/Planck20_Hillipop_TT/__init__.py

@@ -0,0 +1 @@
+from .Planck20_Hillipop_TT import Planck20_Hillipop_TT