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Do not access SharedArray.array directly anymore but rather use the fact that SharedArray behaves somewhat like a Numpy array for basical operations.

Store the model fluxes and properties in dictionaries of SharedArray rather than large SharedArray and adapt the rest of the code. It may be slightly slower doing so but it makes memory allocation easier and it allows to retrieve the values of the fluxes/properties much more convenient as we do not have to retain the index.

No need to add the names of the properties as an attribute as they are note used out of the constructor.

Rather than fitting again the best model to retrieve the proper scaling, let's keep it. It takes little memory.

Store the Bayesian estimates in dictionaries of SharedArray rather than in one large SharedArray. This has the advantage to allocate smaller arrays, which is generally easier. Using dictionaries also makes it easier to access the values of a given property. First patch in a series to use dictionaries more extensively.

The standardisation of error bars must be done on the final estimates, not on the estimates for a single block.

Use a much more readable formula to compute the uncertainties. We use the fact that Var(X) = E(Var(X)) + Var(E(X)). At the same time, broadcast the weights array to the same shape as the other arrays in order to simplify the syntax for the computation.

Add a results manager to store the results of the analysis (bayesian and best fit estimates, best chi², etc.). Several such managers can be merged to combine appropriately the analysis of the observations in different blocks. This is the base class. The implementation will come in subsequent patches.