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Setters and getters for the flux, extprop, and intprop dictionaries are too expensive, in particular as they do not provide any real safety in the present case.

Internally store the error on the upper limits as positive values now that we split them away from regular values.

Store the upper limits and the regular fluxes/properties in separate dictionaries so that we do not have to test when fitting whether they are upper limits of not.

Scalar assignments to a RawArray is a very fast operation. Using a getting represents an important overhead. Eliminate it by accessing directly the private attribuate.

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

Expand the SharedArray class so that from the point of view of the rest of the code it behaves like a Numpy array. This is a very minimal implementation that works with the operations we do on RawArrays. Internally we use a RawArray or a Numpy array representation depending on whether the operation done is scalar or vector.

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.

Make the data member of SharedArray more robust by adding a setter. This setter checks that the variable to be assigned is indeed a ctypes.Array. This prevents from accidentally erasing data with a Numpy array for instance.

rename varibles in  _check_filters (observations.py) to reflect that  properties are also included, fixes documentation

Modify ObservationsManagerPassbands to iterate over an instance of observationclass,  modify Workers accordingly and to include the properties and modify pdf_anaysis/utils to include properties in compute_chi2, _cimpute_scaling and dchi2_over_ds2

Include properties in _check_filters, _check_errors, _check_invalid (only extensive) and add_model_error functions in observations.py. rename errors as bands_errors

Add ParameterManager as input to ObserbationsManagerPassbands class, this is use to separate the intensive properties to fit from the extensive and add them to the class. Modify pdf_analysis/__init__.py to call ParameterManager befor ObservationsManager

modify models.py to use get_info from utils, add intprops and extprops property to ModelsManager class