soma.foo.schema() -> soma.foo.tiledb_array_schema()

apis/python/src/tiledbsc/annotation_dataframe.py

@@ -61,9 +61,9 @@ def ids(self) -> List[str]:
     def keys(self) -> List[str]:
         """
         Returns the column names for the `obs` or `var` dataframe.  For obs and varp, `.keys()` is a
-        keystroke-saver for the more general array-schema accessor `get_attr_names`.
+        keystroke-saver for the more general array-schema accessor `attr_names`.
         """
-        return self.get_attr_names()
+        return self.attr_names()
 
     # ----------------------------------------------------------------
     def dim_select(self, ids):

apis/python/src/tiledbsc/assay_matrix.py

@@ -45,13 +45,6 @@ def __init__(
     # We don't have a .shape() method since X is sparse. One should
     # instead use the row-counts for the soma's obs and var.
 
-    # ----------------------------------------------------------------
-    def dim_names(self):
-        """
-        Keystroke-saving accessor for `row_dim_name` and `col_dim_name`.
-        """
-        return (self.row_dim_name, self.col_dim_name)
-
     # ----------------------------------------------------------------
     def dim_select(self, obs_ids, var_ids):
         """

apis/python/src/tiledbsc/tiledb_array.py

@@ -3,12 +3,14 @@
 from .tiledb_object import TileDBObject
 from .tiledb_group import TileDBGroup
 
-from typing import Optional, List
+from typing import Optional, List, Dict
 
 
 class TileDBArray(TileDBObject):
     """
     Wraps arrays from TileDB-Py by retaining a URI, verbose flag, etc.
+    Also serves as an abstraction layer to hide TileDB-specific details from the API, unless
+    requested.
     """
 
     def __init__(
@@ -37,29 +39,47 @@ def open(self):
         A = tiledb.open(self.uri)
         return A
 
-    def schema(self):
+    def tiledb_array_schema(self):
         """
         Returns the TileDB array schema.
         """
         with tiledb.open(self.uri) as A:
             return A.schema
 
-    def get_dim_names(self) -> List[str]:
+    def dim_names(self) -> List[str]:
         """
         Reads the dimension names from the schema: for example, ['obs_id', 'var_id'].
         """
         with tiledb.open(self.uri) as A:
             return [A.schema.domain.dim(i).name for i in range(A.schema.domain.ndim)]
 
-    def get_attr_names(self) -> List[str]:
+    def dim_names_to_types(self) -> Dict[str, str]:
+        """
+        Returns a dict mapping from dimension name to dimension type.
+        """
+        with tiledb.open(self.uri) as A:
+            dom = A.schema.domain
+            return {dom.dim(i).name: dom.dim(i).dtype for i in range(dom.ndim)}
+
+    def attr_names(self) -> List[str]:
         """
         Reads the attribute names from the schema: for example, the list of column names in a dataframe.
         """
         with tiledb.open(self.uri) as A:
             return [A.schema.attr(i).name for i in range(A.schema.nattr)]
 
+    def attr_names_to_types(self) -> Dict[str, str]:
+        """
+        Returns a dict mapping from attribute name to attribute type.
+        """
+        with tiledb.open(self.uri) as A:
+            schema = A.schema
+            return {
+                schema.attr(i).name: schema.attr(i).dtype for i in range(schema.nattr)
+            }
+
     def has_attr_name(self, attr_name: str) -> bool:
         """
         Returns true if the array has the specified attribute name, false otherwise.
         """
-        return attr_name in self.get_attr_names()
+        return attr_name in self.attr_names()

apis/python/tests/test_soma_group_indexing.py

@@ -50,9 +50,9 @@ def test_soma_group_indexing(h5ad_file):
         ["uns", "varm", "X", "raw", "obsp", "varp", "var", "obsm", "obs"]
     )
     assert set(soma.X.get_member_names()) == set(["data"])
-    assert soma.X.data.get_dim_names() == ["obs_id", "var_id"]
+    assert soma.X.data.dim_names() == ["obs_id", "var_id"]
 
-    assert soma.obs.get_dim_names() == ["obs_id"]
+    assert soma.obs.dim_names() == ["obs_id"]
     assert soma.obs.dim_name == "obs_id"
     assert soma.obs.keys() == [
         "orig.ident",
@@ -159,7 +159,7 @@ def test_soma_group_indexing(h5ad_file):
         np.dtype("int32"),
     ]
 
-    assert soma.var.get_dim_names() == ["var_id"]
+    assert soma.var.dim_names() == ["var_id"]
     assert soma.obs.dim_name == "obs_id"
     assert soma.var.keys() == [
         "vst.mean",
@@ -206,7 +206,7 @@ def test_soma_group_indexing(h5ad_file):
     assert set(soma.obsm.keys()) == set(["X_pca", "X_tsne"])
     assert isinstance(soma.obsm["X_pca"], tiledbsc.AnnotationMatrix)
     assert soma.obsm["nonesuch"] is None
-    assert soma.obsm["X_pca"].get_dim_names() == ["obs_id"]
+    assert soma.obsm["X_pca"].dim_names() == ["obs_id"]
     assert soma.obsm["X_pca"].df().shape == (80, 20)
     assert list(soma.obsm["X_pca"].df().dtypes) == [
         np.dtype("O"),
@@ -235,12 +235,12 @@ def test_soma_group_indexing(h5ad_file):
     assert isinstance(soma.varm["PCs"], tiledbsc.AnnotationMatrix)
     assert soma.varm["nonesuch"] is None
     assert soma.varm.get_member_names() == ["PCs"]
-    assert soma.varm["PCs"].get_dim_names() == ["var_id"]
+    assert soma.varm["PCs"].dim_names() == ["var_id"]
 
     assert set(soma.obsp.get_member_names()) == set(["distances"])
     assert isinstance(soma.obsp["distances"], tiledbsc.AnnotationPairwiseMatrix)
     assert soma.varp["nonesuch"] is None
-    assert soma.obsp["distances"].get_dim_names() == ["obs_id_i", "obs_id_j"]
+    assert soma.obsp["distances"].dim_names() == ["obs_id_i", "obs_id_j"]
 
     assert set(soma.uns.get_member_names()) == set(["neighbors"])
     assert isinstance(soma.uns["neighbors"], tiledbsc.UnsGroup)