Table.match_type: Don't flatten subarrays

Orange/data/table.py

@@ -268,15 +268,27 @@ def from_table(cls, domain, source, row_indices=...):
 
         def get_columns(row_indices, src_cols, n_rows, dtype=np.float64,
                         is_sparse=False):
-            def match_type(x):
-                """ Assure that matrix and column are both dense or sparse. """
+            def match_type(x, force_1d=False):
+                """ Assure that matrix and column are both dense or sparse.
+                
+                Args:
+                    x (np.ndarray, scipy.sparse): data 
+                    force_1d (bool): If set, flatten resulting array to 1d.
+
+                Returns:
+                    array of correct density. 
+                """
                 if is_sparse == sp.issparse(x):
                     return x
-                elif is_sparse:
+                if is_sparse:
                     x = np.asarray(x)
                     return sp.csc_matrix(x.reshape(-1, 1).astype(np.float))
-                else:
-                    return np.ravel(x.toarray())
+                x = x.toarray()
+                if force_1d:
+                    x = np.ravel(x)
+                return x
+
+            match_type_1d = lambda x: match_type(x, force_1d=True)
 
             if not len(src_cols):
                 if is_sparse:
@@ -314,22 +326,22 @@ def match_type(x):
                             shared_cache[id(col.compute_shared), id(source)] = col.compute_shared(source)
                         shared = shared_cache[id(col.compute_shared), id(source)]
                         if row_indices is not ...:
-                            a[:, i] = match_type(
+                            a[:, i] = match_type_1d(
                                 col(source, shared_data=shared)[row_indices])
                         else:
-                            a[:, i] = match_type(
+                            a[:, i] = match_type_1d(
                                 col(source, shared_data=shared))
                     else:
                         if row_indices is not ...:
-                            a[:, i] = match_type(col(source)[row_indices])
+                            a[:, i] = match_type_1d(col(source)[row_indices])
                         else:
-                            a[:, i] = match_type(col(source))
+                            a[:, i] = match_type_1d(col(source))
                 elif col < 0:
-                    a[:, i] = match_type(source.metas[row_indices, -1 - col])
+                    a[:, i] = match_type_1d(source.metas[row_indices, -1 - col])
                 elif col < n_src_attrs:
-                    a[:, i] = match_type(source.X[row_indices, col])
+                    a[:, i] = match_type_1d(source.X[row_indices, col])
                 else:
-                    a[:, i] = match_type(
+                    a[:, i] = match_type_1d(
                         source._Y[row_indices, col - n_src_attrs])
 
             if is_sparse: