TST: Adapt Wasserstein test to new interface after refactor.

tests/unit/test_wasserstein.py

@@ -75,28 +75,21 @@
 # Linearization
 newton_options = {
     # Scheme
-    "L": 1e-9,
+    "L": 1e9,
 }
 bregman_std_options = {
     # Scheme
     "L": 1,
 }
-bregman_reordered_options = {
-    # Scheme
-    "L": 1,
-    "bregman_mode": "reordered",
-}
 bregman_adaptive_options = {
     # Scheme
     "L": 1,
-    "bregman_mode": "adaptive",
-    "bregman_update_cond": lambda iter: iter % 20 == 0,
+    "bregman_update": lambda iter: iter % 20 == 0,
 }
 linearizations = {
     "newton": [newton_options],
     "bregman": [
         bregman_std_options,
-        bregman_reordered_options,
         bregman_adaptive_options,
     ],
 }
@@ -117,27 +110,24 @@
 # Linear solver
 lu_options = {
     # Linear solver
-    "linear_solver": "lu"
+    "linear_solver": "direct",
 }
 amg_options = {
-    "linear_solver": "amg-potential",
-    "linear_solver_tol": 1e-8,
+    "linear_solver": "amg",
+    "linear_solver_options": {
+        "tol": 1e-8,
+    },
 }
 solvers = [lu_options, amg_options]
 
 # General options
 options = {
-    # Solver parameters
-    "regularization": 1e-16,
-    # Scheme
-    "lumping": True,
     # Performance control
     "num_iter": 400,
     "tol_residual": 1e-10,
     "tol_increment": 1e-6,
     "tol_distance": 1e-10,
-    # Output
-    "verbose": False,
+    "return_info": True,
 }
 
 # ! ---- Tests ----
@@ -151,15 +141,14 @@ def test_newton(a_key, s_key, dim):
     options.update(newton_options)
     options.update(accelerations[a_key])
     options.update(solvers[s_key])
-    distance, _, _, _, status = darsia.wasserstein_distance(
+    distance, info = darsia.wasserstein_distance(
         src_image[dim],
         dst_image[dim],
         options=options,
         method="newton",
-        return_solution=True,
     )
     assert np.isclose(distance, true_distance[dim], atol=1e-5)
-    assert status["converged"]
+    assert info["converged"]
 
 
 @pytest.mark.parametrize("a_key", range(len(accelerations)))
@@ -170,34 +159,14 @@ def test_std_bregman(a_key, s_key, dim):
     options.update(bregman_std_options)
     options.update(accelerations[a_key])
     options.update(solvers[s_key])
-    distance, _, _, _, status = darsia.wasserstein_distance(
-        src_image[dim],
-        dst_image[dim],
-        options=options,
-        method="bregman",
-        return_solution=True,
-    )
-    assert np.isclose(distance, true_distance[dim], atol=1e-2)  # TODO
-    assert status["converged"]
-
-
-@pytest.mark.parametrize("a_key", range(len(accelerations)))
-@pytest.mark.parametrize("s_key", range(len(solvers)))
-@pytest.mark.parametrize("dim", [2, 3])
-def test_reordered_bregman(a_key, s_key, dim):
-    """Test all combinations for reordered Bregman."""
-    options.update(bregman_reordered_options)
-    options.update(accelerations[a_key])
-    options.update(solvers[s_key])
-    distance, _, _, _, status = darsia.wasserstein_distance(
+    distance, info = darsia.wasserstein_distance(
         src_image[dim],
         dst_image[dim],
         options=options,
         method="bregman",
-        return_solution=True,
     )
-    assert np.isclose(distance, true_distance[dim], atol=1e-2)  # TODO
-    assert status["converged"]
+    assert np.isclose(distance, true_distance[dim], atol=1e-2)
+    assert info["converged"]
 
 
 @pytest.mark.parametrize("a_key", range(len(accelerations)))
@@ -208,12 +177,11 @@ def test_adaptive_bregman(a_key, s_key, dim):
     options.update(bregman_adaptive_options)
     options.update(accelerations[a_key])
     options.update(solvers[s_key])
-    distance, _, _, _, status = darsia.wasserstein_distance(
+    distance, info = darsia.wasserstein_distance(
         src_image[dim],
         dst_image[dim],
         options=options,
         method="bregman",
-        return_solution=True,
     )
     assert np.isclose(distance, true_distance[dim], atol=1e-5)
-    assert status["converged"]
+    assert info["converged"]