#J = array([0,3,1,2])
#V = array([4,5,7,9])
#A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
# We define the data for the problem, i.e.,
# * the cross sections for node;
# * the length of every node;
# * the boudnary conditions;
# * and the local refinement of every node;
#
prob_data = ProblemData()
# D Sigma_a nu Sigma_f Delta_x
prob_data.set_xs('0',[1/(3*0.416667), 0.334, 1.0, 0.000, 2.7]) #moderator
prob_data.set_xs('1',[1/(3*0.416667), 0.334, 1.0, 0.178, 2.4]) #fuel
prob_data.set_composition(['0', '1', '0', '1', '0', '1', '0'])
prob_data.set_bc(0,0)
prob_data.set_refinements(20)
prob_data.setup()
state = State(prob_data)
matrices = SpatialD(state)
