🎉 (sample/features) add_field: check field size consistency with geometrical support

CHANGELOG.md

@@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- (sample/features) add_field: check field size consistency with geometrical support.
+
 ### Changed
 
 ### Fixes

examples/containers/bench_parallel_load.py

@@ -78,7 +78,8 @@
             # ---# Add some random data
             smp.add_scalar("id", i)
             smp.add_scalar("s0", np.random.randn())
-            smp.add_field("f0", np.random.randn(100))
+            n_nodes = smp.get_nodes().shape[0]
+            smp.add_field("f0", np.random.randn(n_nodes))
 
             dset.add_sample(smp)
 

examples/containers/sample_example.py

@@ -172,7 +172,7 @@ def show_sample(sample: Sample):
 
 # %%
 # Init CGNS tree zone to a base at time 0.
-shape = np.array((len(points), len(triangles), 0))
+shape = np.array([[len(points), len(triangles), 0]])
 sample.init_zone(shape, zone_name="TestZoneName", base_name="SurfaceMesh", time=0.0)
 
 show_sample(sample)
@@ -454,7 +454,7 @@ def show_sample(sample: Sample):
 
 # Create a new zone in 'SurfaceMesh' base
 sample.init_zone(
-    zone_shape=np.array([5, 3, 0]),
+    zone_shape=np.array([[5, 3, 0]]),
     zone_type=CGK.Structured_s,
     zone_name="new_zone",
     base_name="SurfaceMesh",

examples/convert_users_data_example.py

@@ -169,7 +169,7 @@ def in_notebook():
 
     # Add random field values to the sample
     for j, sname in enumerate(out_fields_names):
-        sample.add_field(sname, np.random.rand(1, len(nodes_3D)))
+        sample.add_field(sname, np.random.rand(len(nodes_3D)))
 
     samples.append(sample)
 

examples/utils/stats_example.py

@@ -130,8 +130,9 @@ def sprint(stats: dict):
 for sample in samples:
     sample.add_scalar("test_scalar", np.random.randn())
     sample.init_base(2, 3, "test_base")
-    zone_shape = np.array([0, 0, 0])
+    zone_shape = np.array([[spatial_shape_max, 0, 0]])
     sample.init_zone(zone_shape, zone_name="test_zone")
+    sample.set_nodes(np.zeros((spatial_shape_max, 3)))
     sample.add_field("test_field", np.random.randn(spatial_shape_max))
 
 stats.add_samples(samples)
@@ -154,12 +155,13 @@ def sprint(stats: dict):
 for sample in samples:
     sample.add_scalar("test_scalar", np.random.randn())
     sample.init_base(2, 3, "test_base")
-    zone_shape = np.array([0, 0, 0])
+    zone_shape = np.array([[spatial_shape_max, 0, 0]])
     sample.init_zone(zone_shape, zone_name="test_zone")
-    sample.add_field("test_field_same_size", np.random.randn(7))
+    sample.set_nodes(np.zeros((spatial_shape_max, 3)))
+    sample.add_field("test_field_same_size", np.random.randn(spatial_shape_max))
     sample.add_field(
         "test_field",
-        np.random.randn(np.random.randint(spatial_shape_max // 2, spatial_shape_max)),
+        np.random.randn(spatial_shape_max),
     )
 
 stats.add_samples(samples)

src/plaid/containers/features.py

@@ -1174,6 +1174,10 @@ def add_field(
             KeyError: Raised if the specified zone does not exist in the given base.
         """
         assert isinstance(field, np.ndarray)
+        if field.ndim != 1:
+            raise ValueError(
+                f"field has {field.ndim} dimensions, but must be a 1D array."
+            )
         _check_names([name])
         # init_tree will look for default time
         self.init_tree(time)
@@ -1185,6 +1189,17 @@ def add_field(
                 f"there is no Zone with name {zone_name} in base {base_name}. Did you check topological and physical dimensions ?"
             )
 
+        # Check field size consistency with its geometrical support
+        n_nodes, n_elems, _ = zone_node[1][0]
+        if location == "Vertex" and field.shape[0] != n_nodes:
+            raise ValueError(
+                f"field has {field.shape[0]} nodes but zone has {n_nodes} nodes (based on the zone node metadata)"
+            )
+        elif location == "CellCenter" and field.shape[0] != n_elems:
+            raise ValueError(
+                f"field has {field.shape[0]} nodes but zone has {n_elems} elements (based on the zone node metadata)"
+            )
+
         if field.dtype in (np.int32, np.int64):
             logger.warning(
                 f"(add_field) provided field is of type {field.dtype} and has been converted to np.float64 for CGNS compatibility."

tests/conftest.py

@@ -28,7 +28,9 @@ def generate_samples_no_string(nb: int, zone_name: str, base_name: str) -> list[
     for i in range(nb):
         sample = Sample()
         sample.init_base(3, 3, base_name)
-        sample.init_zone(np.array([0, 0, 0]), zone_name=zone_name, base_name=base_name)
+        sample.init_zone(
+            np.array([[17, 10, 0]]), zone_name=zone_name, base_name=base_name
+        )
         sample.add_scalar("test_scalar", float(i))
         sample.add_scalar("test_scalar_2", float(i**2))
         sample.add_global("global_0", 0.5 + np.ones((2, 3)))
@@ -41,9 +43,10 @@ def generate_samples_no_string(nb: int, zone_name: str, base_name: str) -> list[
         )
         sample.add_field(
             name="test_field_2785",
-            field=float(i**5) * np.ones(3 * (i + 1)),
+            field=float(i**5) * np.ones(10),
             zone_name=zone_name,
             base_name=base_name,
+            location="CellCenter",
         )
         sample_list.append(sample)
     return sample_list
@@ -150,7 +153,7 @@ def muscat_mesh(nodes, triangles, vertex_field, cell_center_field, nodal_tags):
     muscat_mesh = MCT.CreateMeshOfTriangles(nodes, triangles)
     muscat_mesh.GetNodalTag("tag").AddToTag(nodal_tags)
     muscat_mesh.nodeFields["test_node_field_1"] = vertex_field
-    muscat_mesh.nodeFields["big_node_field"] = np.random.randn(50)
+    muscat_mesh.nodeFields["big_node_field"] = np.random.randn(5)
     muscat_mesh.elemFields["test_elem_field_1"] = cell_center_field
     return muscat_mesh
 

tests/containers/test_dataset.py

@@ -344,7 +344,7 @@ def test_get_field_names(self, dataset_with_samples, nb_samples):
         ) == sorted(["test_field_2785", "test_field_same_size"])
         assert sorted(
             dataset_with_samples.get_field_names(location="Vertex")
-        ) == sorted(["test_field_2785", "test_field_same_size"])
+        ) == sorted(["test_field_same_size"])
         assert sorted(
             dataset_with_samples.get_field_names(zone_name="Zone_Name")
         ) == sorted(["test_field_2785", "test_field_same_size"])
@@ -745,13 +745,13 @@ def test_get_tabular_from_homogeneous_identifiers_inconsistent_features_through_
         dataset_with_samples_with_tree_ = copy.deepcopy(dataset_with_samples_with_tree)
         for sample in dataset_with_samples_with_tree_:
             sample.add_field(
-                "test_node_field_1", np.array([0, 1]), warning_overwrite=False
+                "test_node_field_1", np.array([0, 1, 2, 3, 4]), warning_overwrite=False
             )
         with pytest.raises(AssertionError):
             dataset_with_samples_with_tree_.get_tabular_from_homogeneous_identifiers(
                 feature_identifiers=[
                     FeatureIdentifier({"type": "field", "name": "test_node_field_1"}),
-                    FeatureIdentifier({"type": "field", "name": "OriginalIds"}),
+                    FeatureIdentifier({"type": "field", "name": "test_elem_field_1"}),
                 ],
             )
 
@@ -760,16 +760,21 @@ def test_get_tabular_from_homogeneous_identifiers_inconsistent_features_through_
     ):
         dataset_with_samples_with_tree_ = copy.deepcopy(dataset_with_samples_with_tree)
         dataset_with_samples_with_tree_[0].add_field(
-            "test_node_field_1", np.array([0, 1]), warning_overwrite=False
+            "test_node_field_1",
+            np.array([0, 1, 2, 3, 4], dtype=np.float32),
+            warning_overwrite=False,
         )
         dataset_with_samples_with_tree_[0].add_field(
-            "OriginalIds", np.array([0, 1]), warning_overwrite=False
+            "OriginalIds",
+            np.array([0, 1, 2], dtype=np.float32),
+            location="CellCenter",
+            warning_overwrite=False,
         )
         with pytest.raises(AssertionError):
             dataset_with_samples_with_tree_.get_tabular_from_homogeneous_identifiers(
                 feature_identifiers=[
                     FeatureIdentifier({"type": "field", "name": "test_node_field_1"}),
-                    FeatureIdentifier({"type": "field", "name": "OriginalIds"}),
+                    FeatureIdentifier({"type": "field", "name": "test_elem_field_1"}),
                 ],
             )