skip failing m3gnet tests with TODO and link to matgl issue

materialsvirtuallab/matgl#238

pymatgen/transformations/advanced_transformations.py

@@ -338,7 +338,9 @@ def __init__(
         if max_cell_size and max_disordered_sites:
             raise ValueError("Cannot set both max_cell_size and max_disordered_sites!")
 
-    def apply_transformation(self, structure: Structure, return_ranked_list: bool | int = False):
+    def apply_transformation(
+        self, structure: Structure, return_ranked_list: bool | int = False
+    ) -> Structure | list[dict]:
         """Returns either a single ordered structure or a sequence of all ordered
         structures.
 
@@ -879,7 +881,7 @@ def apply_transformation(
             # remove dummy species and replace Spin.up or Spin.down
             # with spin magnitudes given in mag_species_spin arg
             alls = self._remove_dummy_species(alls)
-            alls = self._add_spin_magnitudes(alls)
+            alls = self._add_spin_magnitudes(alls)  # type: ignore[arg-type]
         else:
             for idx in range(len(alls)):
                 alls[idx]["structure"] = self._remove_dummy_species(alls[idx]["structure"])
@@ -891,7 +893,7 @@ def apply_transformation(
             num_to_return = 1
 
         if num_to_return == 1 or not return_ranked_list:
-            return alls[0]["structure"] if num_to_return else alls
+            return alls[0]["structure"] if num_to_return else alls  # type: ignore[return-value]
 
         # remove duplicate structures and group according to energy model
         matcher = StructureMatcher(comparator=SpinComparator())
@@ -1010,11 +1012,10 @@ def apply_transformation(self, structure: Structure, return_ranked_list: bool |
         Args:
             structure (Structure): Input structure to dope
             return_ranked_list (bool | int, optional): If return_ranked_list is int, that number of structures.
-
                 is returned. If False, only the single lowest energy structure is returned. Defaults to False.
 
         Returns:
-            [{"structure": Structure, "energy": float}]
+            list[dict] | Structure: each dict has shape {"structure": Structure, "energy": float}.
         """
         comp = structure.composition
         logger.info(f"Composition: {comp}")
@@ -1059,7 +1060,7 @@ def apply_transformation(self, structure: Structure, return_ranked_list: bool |
         logger.info(f"{lengths=}")
         logger.info(f"{scaling=}")
 
-        all_structures = []
+        all_structures: list[dict] = []
         trafo = EnumerateStructureTransformation(**self.kwargs)
 
         for sp in compatible_species:
@@ -1131,10 +1132,9 @@ def apply_transformation(self, structure: Structure, return_ranked_list: bool |
                     }
                 )
 
-            ss = trafo.apply_transformation(supercell, return_ranked_list=self.max_structures_per_enum)
-            logger.info(f"{len(ss)} distinct structures")
-            all_structures.extend(ss)
-
+            structs = trafo.apply_transformation(supercell, return_ranked_list=self.max_structures_per_enum)
+            logger.info(f"{len(structs)} distinct structures")
+            all_structures.extend(structs)
         logger.info(f"Total {len(all_structures)} doped structures")
         if return_ranked_list:
             return all_structures[:return_ranked_list]

tests/core/test_structure.py

@@ -1705,6 +1705,7 @@ def test_relax_ase_opt_kwargs(self):
         assert traj[0] != traj[-1]
         assert os.path.isfile(traj_file)
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_calculate_m3gnet(self):
         pytest.importorskip("matgl")
         calculator = self.get_structure("Si").calculate()
@@ -1716,6 +1717,7 @@ def test_calculate_m3gnet(self):
         assert np.linalg.norm(calculator.results["forces"]) == approx(7.8123485e-06, abs=0.2)
         assert np.linalg.norm(calculator.results["stress"]) == approx(1.7861567, abs=2)
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_relax_m3gnet(self):
         pytest.importorskip("matgl")
         struct = self.get_structure("Si")
@@ -1726,6 +1728,7 @@ def test_relax_m3gnet(self):
             actual = relaxed.dynamics[key]
             assert actual == val, f"expected {key} to be {val}, {actual=}"
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_relax_m3gnet_fixed_lattice(self):
         pytest.importorskip("matgl")
         struct = self.get_structure("Si")
@@ -1734,6 +1737,7 @@ def test_relax_m3gnet_fixed_lattice(self):
         assert hasattr(relaxed, "calc")
         assert relaxed.dynamics["optimizer"] == "BFGS"
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_relax_m3gnet_with_traj(self):
         pytest.importorskip("matgl")
         struct = self.get_structure("Si")

tests/transformations/test_advanced_transformations.py

@@ -189,6 +189,7 @@ def test_apply_transformation(self):
         for s in alls:
             assert "energy" not in s
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_m3gnet(self):
         pytest.importorskip("matgl")
         enum_trans = EnumerateStructureTransformation(refine_structure=True, sort_criteria="m3gnet_relax")
@@ -204,6 +205,7 @@ def test_m3gnet(self):
         # Check ordering of energy/atom
         assert alls[0]["energy"] / alls[0]["num_sites"] <= alls[-1]["energy"] / alls[-1]["num_sites"]
 
+    @pytest.mark.skip("TODO remove skip once https://github.com/materialsvirtuallab/matgl/issues/238 is resolved")
     def test_callable_sort_criteria(self):
         matgl = pytest.importorskip("matgl")
         from matgl.ext.ase import Relaxer
@@ -212,8 +214,8 @@ def test_callable_sort_criteria(self):
 
         m3gnet_model = Relaxer(potential=pot)
 
-        def sort_criteria(s):
-            relax_results = m3gnet_model.relax(s)
+        def sort_criteria(struct: Structure) -> tuple[Structure, float]:
+            relax_results = m3gnet_model.relax(struct)
             energy = float(relax_results["trajectory"].energies[-1])
             return relax_results["final_structure"], energy