Raise if params not registered are saved

src/ert/storage/local_ensemble.py

@@ -337,6 +337,9 @@ def save_parameters(
                 f"must contain a 'values' variable"
             )
 
+        if group not in self.experiment.parameter_configuration:
+            raise ValueError(f"{group} is not registered to the experiment.")
+
         path = self.mount_point / f"realization-{realization}" / f"{group}.nc"
         path.parent.mkdir(exist_ok=True)
 

tests/unit_tests/config/test_surface_config.py

@@ -26,7 +26,6 @@ def surface():
 
 
 def test_runpath_roundtrip(tmp_path, storage, surface):
-    ensemble = storage.create_experiment().create_ensemble(name="text", ensemble_size=1)
     config = SurfaceConfig(
         "some_name",
         forward_init=True,
@@ -42,7 +41,9 @@ def test_runpath_roundtrip(tmp_path, storage, surface):
         output_file=tmp_path / "output",
         base_surface_path="base_surface",
     )
-
+    ensemble = storage.create_experiment(parameters=[config]).create_ensemble(
+        name="text", ensemble_size=1
+    )
     surface.to_file(tmp_path / "input_0", fformat="irap_ascii")
 
     # run_path -> storage

tests/unit_tests/storage/migration/test_block_fs_snake_oil.py

@@ -9,7 +9,8 @@
 import ert.storage
 import ert.storage.migration._block_fs_native as bfn
 import ert.storage.migration.block_fs as bf
-from ert.config import ErtConfig
+from ert.config import ErtConfig, GenKwConfig
+from ert.storage import open_storage
 from ert.storage.local_storage import local_storage_set_ert_config
 
 
@@ -61,17 +62,30 @@ def time_map(enspath):
     return bf._load_timestamps(enspath / "default_0/files/time-map")
 
 
-def test_migrate_gen_kw(data, ensemble, parameter, ens_config):
+def test_migrate_gen_kw(data, parameter, ens_config, tmp_path):
     group_root = "/REAL_0/GEN_KW"
-    bf._migrate_gen_kw(ensemble, parameter, ens_config)
+    with open_storage(tmp_path / "storage", mode="w") as storage:
+        experiment = storage.create_experiment(
+            parameters=[
+                GenKwConfig(
+                    name="SNAKE_OIL_PARAM",
+                    forward_init=False,
+                    template_file="",
+                    transfer_function_definitions=[],
+                    output_file="kw.txt",
+                )
+            ]
+        )
+        ensemble = experiment.create_ensemble(name="default_0", ensemble_size=5)
+        bf._migrate_gen_kw(ensemble, parameter, ens_config)
 
-    for param in ens_config.parameters:
-        expect_names = list(data[f"{group_root}/{param}"]["name"])
-        expect_array = np.array(data[f"{group_root}/{param}"]["standard_normal"])
-        actual = ensemble.load_parameters(param, 0)
+        for param in ens_config.parameters:
+            expect_names = list(data[f"{group_root}/{param}"]["name"])
+            expect_array = np.array(data[f"{group_root}/{param}"]["standard_normal"])
+            actual = ensemble.load_parameters(param, 0)
 
-        assert expect_names == list(actual["names"]), param
-        assert (expect_array == actual).all(), param
+            assert expect_names == list(actual["names"]), param
+            assert (expect_array == actual).all(), param
 
 
 def test_migrate_summary(data, ensemble, forecast, time_map):

tests/unit_tests/storage/test_local_ensemble.py

@@ -4,6 +4,8 @@
 import xtgeo
 from resdata.grid import GridGenerator
 
+from ert.config.field import Field
+from ert.field_utils import FieldFileFormat
 from ert.storage import open_storage
 
 
@@ -13,8 +15,27 @@ def test_that_egrid_files_are_saved_and_loaded_correctly(tmp_path):
         mask = grid.get_actnum()
         mask_values = [True] * 3 + [False] * 16 + [True]
         mask.values = mask_values
+        grid_file = str(tmp_path / "grid.EGRID")
+        grid.to_file(grid_file, fformat="egrid")
+        param_group = "MY_PARAM"
+
+        field_config = Field(
+            name=param_group,
+            forward_init=True,
+            nx=grid.nrow,
+            ny=grid.ncol,
+            nz=grid.nlay,
+            file_format=FieldFileFormat.GRDECL,
+            output_transformation=None,
+            input_transformation=None,
+            truncation_min=None,
+            truncation_max=None,
+            forward_init_file="",
+            output_file="",
+            grid_file=grid_file,
+        )
 
-        experiment = storage.create_experiment()
+        experiment = storage.create_experiment(parameters=[field_config])
         ensemble = storage.create_ensemble(experiment, name="foo", ensemble_size=2)
         ensemble_dir = tmp_path / "ensembles" / str(ensemble.id)
         assert ensemble_dir.exists()
@@ -34,31 +55,49 @@ def test_that_egrid_files_are_saved_and_loaded_correctly(tmp_path):
 
 
 def test_that_grid_files_are_saved_and_loaded_correctly(tmp_path):
-    with open_storage(tmp_path, mode="w") as storage:
-        experiment = storage.create_experiment()
-        ensemble = storage.create_ensemble(experiment, name="foo", ensemble_size=2)
-        ensemble_dir = tmp_path / "ensembles" / str(ensemble.id)
-        assert ensemble_dir.exists()
-
+    with open_storage(tmp_path / "storage", mode="w") as storage:
         mask = [True] * 3 + [False] * 16 + [True]
         grid = GridGenerator.create_rectangular((4, 5, 1), (1, 1, 1), actnum=mask)
-        grid.save_GRID(f"{experiment.mount_point}/grid.GRID")
+        grid_file = str(storage.path / "grid.GRID")
+        grid.save_GRID(grid_file)
+        param_group = "MY_PARAM"
+        field_config = Field(
+            name=param_group,
+            forward_init=True,
+            nx=grid.nx,
+            ny=grid.ny,
+            nz=grid.nz,
+            file_format=FieldFileFormat.GRDECL,
+            output_transformation=None,
+            input_transformation=None,
+            truncation_min=None,
+            truncation_max=None,
+            forward_init_file="",
+            output_file="",
+            grid_file=grid_file,
+        )
+        experiment = storage.create_experiment(parameters=[field_config])
+        ensemble = storage.create_ensemble(experiment, name="foo", ensemble_size=2)
+        ensemble_dir = tmp_path / "storage" / "ensembles" / str(ensemble.id)
+        assert ensemble_dir.exists()
 
         data = np.full_like(mask, np.nan, dtype=np.float32)
         np.place(data, mask, np.array([1.2, 1.1, 4.3, 3.1], dtype=np.float32))
         da = xr.DataArray(
-            data.reshape((4, 5, 1)),
+            data.reshape((grid.nx, grid.ny, grid.nz)),
             name="values",
             dims=["x", "y", "z"],  # type: ignore
         )
         ds = da.to_dataset()
-        ensemble.save_parameters("MY_PARAM", 1, ds)
+        ensemble.save_parameters(param_group, 1, ds)
 
-        saved_file = ensemble_dir / "realization-1" / "MY_PARAM.nc"
+        saved_file = ensemble_dir / "realization-1" / f"{param_group}.nc"
         assert saved_file.exists()
 
-        loaded_data = ensemble.load_parameters("MY_PARAM", 1)
-        np.testing.assert_array_equal(loaded_data.values, data.reshape((4, 5, 1)))
+        loaded_data = ensemble.load_parameters(param_group, 1)
+        np.testing.assert_array_equal(
+            loaded_data.values, data.reshape((grid.nx, grid.ny, grid.nz))
+        )
 
 
 def test_that_load_responses_throws_exception(tmp_path):
@@ -77,3 +116,29 @@ def test_that_load_parameters_throws_exception(tmp_path):
 
         with pytest.raises(expected_exception=KeyError):
             ensemble.load_parameters("I_DONT_EXIST", 1)
+
+
+def test_that_only_registered_parameters_can_be_saved(tmp_path):
+    with open_storage(tmp_path, mode="w") as storage:
+        experiment = storage.create_experiment()
+        prior = storage.create_ensemble(
+            experiment,
+            ensemble_size=1,
+            iteration=0,
+            name="prior",
+        )
+
+        with pytest.raises(
+            ValueError, match="PARAMETER is not registered to the experiment."
+        ):
+            prior.save_parameters(
+                "PARAMETER",
+                0,
+                xr.Dataset(
+                    {
+                        "values": ("names", [1.0]),
+                        "transformed_values": ("names", [1.0]),
+                        "names": ["KEY_1"],
+                    }
+                ),
+            )