How to use the amici.numpy.ExpDataView function in amici

obj.amici_solver.setSensitivityOrder(sensi_order)

        # run amici simulation
        rdatas = amici.runAmiciSimulations(
            obj.amici_model,
            obj.amici_solver,
            obj.edatas,
            num_threads=min(obj.n_threads, len(obj.edatas)),
        )

        # check if any simulation failed
        if any([rdata['status'] < 0.0 for rdata in rdatas]):
            return obj.get_error_output(rdatas)

        # edatas to numpy arrays (TODO cache this?)
        edatas = [amici.numpy.ExpDataView(edata)
                  for edata in obj.edatas]

        # compute optimal parameters (code base same for fw+ad)

        # create matrices from optimal parameters (only fw)

        # compute objective and derivatives (only fw)

        # compute optimal parameters
        #optimal_scalings = compute_optimal_scaling_matrix(self.problem, edatas, rdatas)
        # compute optimal surrogate data
        if self.problem.optimalScalingOptions['timeInnerProblem']:
            import time
            from datetime import date
            num_repeat = 50
            #edatas = [amici.numpy.ExpDataView(edata)

def qualitative_inner_problem_from_petab_problem(
        petab_problem: petab.Problem,
        amici_model: 'amici.Model',
        edatas: List['amici.ExpData']):
    # inner parameters
    inner_parameters = inner_parameters_from_parameter_df(
        petab_problem.parameter_df)

    x_ids = [x.id for x in inner_parameters]

    # used indices for all measurement specific parameters
    ixs = ixs_for_measurement_specific_parameters(
        petab_problem, amici_model, x_ids)
    # print(ixs)
    # transform experimental data
    edatas = [amici.numpy.ExpDataView(edata)['observedData']
              for edata in edatas]
    # print(edatas)
    # matrixify
    ix_matrices = ix_matrices_from_arrays(ixs, edatas)
    # print(ix_matrices)
    # assign matrices
    for par in inner_parameters:
        par.ixs = ix_matrices[par.id]

    return OptimalScalingProblem(inner_parameters, edatas)