handle no-column object table; WIP unit test

src/tape/ensemble.py

@@ -1297,6 +1297,13 @@ def save_ensemble(self, path=".", dirname="ensemble", additional_frames=True, **
             # grab the dataframe from the frame label
             frame = self.frames[frame_label]
 
+            # Object can have no columns, which parquet doesn't handle
+            # In this case, we'll avoid saving to parquet
+            if frame_label == "object":
+                if len(frame.columns) == 0:
+                    print("The Object Frame was not saved as no columns were present.")
+                    continue
+
             # creates a subdirectory for the frame partition files
             frame.to_parquet(os.path.join(ens_path, frame_label), **kwargs)
 
@@ -1342,8 +1349,13 @@ def from_ensemble(self, dirpath, additional_frames=True, column_mapper=None, **k
 
         # Load Object and Source
         obj_path = os.path.join(dirpath, "object")
-        src_path = os.path.join(dirpath, "object")
-        self.from_parquet(src_path, obj_path, column_mapper=column_mapper, **kwargs)
+        src_path = os.path.join(dirpath, "source")
+
+        # Check for whether or not object is present, it's not saved when no columns are present
+        if "object" in os.listdir(dirpath):
+            self.from_parquet(src_path, obj_path, column_mapper=column_mapper, **kwargs)
+        else:
+            self.from_parquet(src_path, column_mapper=column_mapper, **kwargs)
 
         # Load all remaining frames
         if additional_frames is False:

tests/tape_tests/test_ensemble.py

@@ -478,6 +478,42 @@ def test_read_source_dict(dask_client):
     assert 8002 in obj_table.index
 
 
+def test_save_and_load_ensemble(dask_client):
+    # Set a seed for reproducibility
+    np.random.seed(1)
+
+    # Create some toy data
+    obj_ids = np.array([])
+    mjds = np.array([])
+    for i in range(10, 110):
+        obj_ids = np.append(obj_ids, np.array([i] * 1250))
+        mjds = np.append(mjds, np.arange(0.0, 1250.0, 1.0))
+    obj_ids = np.array(obj_ids)
+    flux = 10 * np.random.random(125000)
+    err = flux / 10
+    band = np.random.choice(["g", "r"], 125000)
+
+    # Store it in a dictionary
+    source_dict = {"id": obj_ids, "mjd": mjds, "flux": flux, "err": err, "band": band}
+
+    # Create an Ensemble
+    ens = Ensemble()
+    ens.from_source_dict(
+        source_dict,
+        column_mapper=ColumnMapper(
+            id_col="id", time_col="mjd", flux_col="flux", err_col="err", band_col="band"
+        ),
+    )
+
+    # Make a column for the object table
+    ens.calc_nobs(temporary=False)
+    # Add a few result frames
+    ens.batch(np.mean, "flux", label="mean")
+    ens.batch(np.max, "flux", label="max")
+
+    ens.save_ensemble("./ensemble")
+
+
 def test_insert(parquet_ensemble):
     num_partitions = parquet_ensemble.source.npartitions
     (old_object, old_source) = parquet_ensemble.compute()