In particular we should test that the prescribed permutations for the existing baseline tests result in final (rebalanced) meshes with the expected value for W_max
For example, if we run the following test case (which is the classic load-only) with CCM-MILP:
# Solution summary:
Rank 0: L = 2.0, W = 2.0, unhomed: 1
Rank 1: L = 2.0, W = 2.0, unhomed: 1
Rank 2: L = 2.0, W = 2.0, unhomed: 1
Rank 3: L = 2.0, W = 2.0, unhomed: 2
W_max = 2.0
$assignments=[0, 0, 1, 1, 0, 2, 1, 3, 3];
In turn we should "feed" this assignment as input to the PrescribedPermutationAlgorithm (use the permutate.yaml config file as a template), and the execution should yield an output with the same value of W_max (i.e. 2.0 in this case).
This is a follow-on to #544 (PR #545 )
In particular we should test that the prescribed permutations for the existing baseline tests result in final (rebalanced) meshes with the expected value for
W_max
For example, if we run the following test case (which is the classic
load-only
) with CCM-MILP:we obtain the following results:
In turn we should "feed" this assignment as input to the
PrescribedPermutationAlgorithm
(use thepermutate.yaml
config file as a template), and the execution should yield an output with the same value ofW_max
(i.e.2.0
in this case).