Add column for storing visualization embeddings

src/hdmf_ai/results_table.py

@@ -454,3 +454,35 @@ def add_embedding(self, **kwargs):
         # `n_dims` kwarg is passed into `__add_col` and will be read as the length of the second dimension
         # of the data only if the `data` kwarg is None.
         return self.__add_col(EmbeddedValues, **kwargs)
+
+    @docval(
+        {
+            "name": "data",
+            "type": ("array_data", "data"),
+            "doc": "Embedding (float) of each sample.",
+            "default": None,
+        },
+        {
+            "name": "description",
+            "type": str,
+            "doc": "A description for this column.",
+            "default": "A column to store embeddings, e.g., from dimensionality reduction, for each sample.",
+        },
+        {
+            "name": "n_dims",
+            "type": int,
+            "doc": (
+                "The number of dimensions in the embedding, "
+                "used to define the shape of the column only if data is None"
+            ),
+            "default": None,
+        },
+    )
+    def add_viz_embedding(self, **kwargs):
+        """Add embedding (a.k.a. transformation or representation) of each sample."""
+        kwargs["name"] = "viz_embedding"
+        kwargs["dtype"] = float
+        kwargs["dim2_kwarg"] = "n_dims"
+        # `n_dims` kwarg is passed into `__add_col` and will be read as the length of the second dimension
+        # of the data only if the `data` kwarg is None.
+        return self.__add_col(EmbeddedValues, **kwargs)

src/hdmf_ai/schema/results_table.yaml

@@ -185,4 +185,8 @@ groups:
     data_type_inc: EmbeddedValues
     doc: A column to store embeddings, e.g., from dimensionality reduction, for each sample.
     quantity: '?'
+  - name: viz_embedding
+    data_type_inc: EmbeddedValues
+    doc: A column to store embeddings meant for visualization.
+    quantity: '?'
 

tests/test_results_table.py

@@ -122,6 +122,12 @@ def test_add_embedding(self):
         with self.get_hdf5io() as io:
             io.write(rt)
 
+    def test_add_viz_embedding(self):
+        rt = ResultsTable(name="foo", description="a test results table")
+        rt.add_viz_embedding([[1.1, 2.9], [1.2, 2.8], [1.3, 2.7], [1.4, 2.6], [1.5, 2.5]])
+        with self.get_hdf5io() as io:
+            io.write(rt)
+
     def test_add_topk_classes(self):
         rt = ResultsTable(name="foo", description="a test results table")
         rt.add_topk_classes([[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]])