None or True if success, False for fail get_Cl ( ell_factor = False, units = 'FIRASmuK2' ) ¶ Want_derived – whether to set state derived parameters calculate ( state, want_derived = True, ** params_values_dict ) ¶ĭo the actual calculation and store results in state dict Parameters CAMB ( info = mappingproxy(), d = 1 ) ¶Ĭreates a collector for a z-dependent quantity, keeping track of the pool of z’s. Python setup.py install, as the official instructions suggest.
Modifications of CAMB you should not install CAMB as python package using In any of these methods, if you intend to switch between different versions or To check if you have the latter, type gfortran -version in the shell, Using intel’s ifort compiler or the GNU gfortran compiler version 6.4 or later. Installation ¶ Pre-requisites ¶Ĭobaya calls CAMB using its Python interface, which requires that you compile CAMB Whenever such an error is raised during sampling, the likelihood isĪssumed to be zero, and the run is not interrupted. In your CAMB modification, remember that you can raise a CAMBParamRangeError or aĬAMBError whenever the computation of any observable would fail, but you do notĮxpect that observable to be compatible with the data (e.g. Space (set debug: True to get more detailed information of what exactly is passed to You can use the model wrapper to test your modification byĮvaluating observables or getting derived quantities at known points in the parameter If you follow those instructions you do not need to make any additional modification in If you modify CAMB and add new variables, make sure that the variables you create areĮxposed in the Python interface ( instructions here). Initialisation where it is getting CAMB from. Otherwise imported as a globally-installed Python package. If you do not specify a path,ĬAMB will be loaded from the automatic-install packages_path folder, if specified, or If you want to use your own version of CAMB, you need to specify its location with a
Conda install gfortran manual#
Manual installation (or using your own version).Creating your own cosmological likelihood class.Creating your own cosmological likelihood.Cosmological theory codes and likelihoods.Importance reweighting and general post-processing.Creating theory classes and dependencies.Models: finer interaction with Cobaya’s pipeline.