When defining many FastFit instances with several observables and measurements, it might be useful to not only save the SM covariance matrix, but also the experimental covariance matrix and the corresponding experimental central values. The necessary FastFit methods to do this are provided by this PR (mainly based on the methods for saving and loading the SM covariance).
Coverage decreased (-0.007%) to 90.617% when pulling a649fc0c55a8d7894a6a4462ff901db86fd1c954 on peterstangl:save_exp_covariance into 953599969bd4a2d33d1d326342f0b0094b957775 on flav-io:master.
When defining many
FastFit
instances with several observables and measurements, it might be useful to not only save the SM covariance matrix, but also the experimental covariance matrix and the corresponding experimental central values. The necessaryFastFit
methods to do this are provided by this PR (mainly based on the methods for saving and loading the SM covariance).