FEAT: add skeleton for amico implementation

[matteofrigo]

With this PR we add the skeleton for the implementation of AMICO. Let's open the discussion @Sara04 @rutgerfick .

dmipy.core.modeling_framework

For the MultiCompartmentAMICOModel and the MultiCompartmentAMICOSphericalMeanModel we have to do the following:

• Implement _create_forward_model_matrix
• Choose a strategy for the solver to use. The difficulty stands in keeping the syntax consistent with the other models
• Update documentation of fit function
• Complete the setup of the optimization before model fitting in the fit function, taking into account that the look-up table must be built or loaded.
• Implement the fit_func method in the fit function
• Preprocess the x0 as required by the solver
• Implement the multi-tissue feature correctly

dmipy.core.fitted_modeling_framework

• I added the fitted_distribution property to the FittedMultiCompartmentAMICOModel and FittedMultiCompartmentAMICOSphericalMeanModel classes. This is intended to return the distribution of each parameter obtained from the AMICO fitting.
• The fitted_parameter property could be built on top of the fitted_distribution.

[rutgerfick]

As for the creation of the AMICO solver, I think we should build the skeleton the same as for https://github.com/AthenaEPI/dmipy/blob/master/dmipy/optimizers_fod/csd_cvxpy.py.

So without a lookup-table, I think in the first iteration we just build the M-matrix on-the-fly in the main call of the class.

In a next iteration we prepare the look-up table etc in the init, and then use that in the call to speed up the process.

However, since we're using multi-processing we have to consider that a lookup table is a big piece of memory and it will be painful to keep creating processes with this block of data, but we'll tackle this problem when we get there.