Add test of updated load_timeseries_csv

Author: znicholls

src/pandas_openscm/io.py

@@ -13,7 +13,7 @@ def load_timeseries_csv(
     fp: Path,
     lower_column_names: bool = True,
     index_columns: list[str] | None = None,
-    out_column_type: type | None = None,
+    out_columns_type: type | None = None,
     out_columns_name: str | None = None,
 ) -> pd.DataFrame:
     """
@@ -43,7 +43,7 @@ def load_timeseries_csv(
         In future, if not provided, we will try and infer the columns
         based on whether they look like time columns or not.
 
-    out_column_type
+    out_columns_type
         The type to apply to the output columns that are not part of the index.
 
         If not supplied, the raw type returned by pandas is returned.
@@ -73,8 +73,8 @@ def load_timeseries_csv(
 
     out = out.set_index(index_columns)
 
-    if out_column_type is not None:
-        out.columns = out.columns.astype(out_column_type)
+    if out_columns_type is not None:
+        out.columns = out.columns.astype(out_columns_type)
 
     if out_columns_name is not None:
         out = out.rename_axis(out_columns_name, axis="columns")

tests/integration/test_io.py

@@ -85,16 +85,16 @@ def test_load_timeseries_csv_lower_column_names(tmp_path, lower_column_names):
 @pytest.mark.parametrize(
     # Column type and value type are not the same
     # because columns are held as numpy arrays.
-    "out_column_type, exp_column_value_type",
+    "out_columns_type, exp_column_value_type",
     (
         (int, np.int64),
         (float, np.float64),
         (np.float64, np.float64),
         (np.float32, np.float32),
     ),
 )
-def test_load_timeseries_csv_basic_out_column_type(
-    tmp_path, out_column_type, exp_column_value_type
+def test_load_timeseries_csv_basic_out_columns_type(
+    tmp_path, out_columns_type, exp_column_value_type
 ):
     out_path = tmp_path / "test_load_timeseries_csv.csv"
 
@@ -113,13 +113,46 @@ def test_load_timeseries_csv_basic_out_column_type(
     index_columns = ["variable", "scenario", "run", "unit"]
 
     loaded = load_timeseries_csv(
-        out_path, index_columns=index_columns, out_column_type=out_column_type
+        out_path, index_columns=index_columns, out_columns_type=out_columns_type
     )
 
     assert loaded.index.names == index_columns
     assert all(isinstance(c, exp_column_value_type) for c in loaded.columns.values)
 
 
+@pytest.mark.parametrize(
+    "out_columns_name, exp_columns_name",
+    (
+        (None, None),
+        ("hi", "hi"),
+        ("time", "time"),
+    ),
+)
+def test_load_timeseries_csv_basic_out_columns_name(
+    tmp_path, out_columns_name, exp_columns_name
+):
+    out_path = tmp_path / "test_load_timeseries_csv.csv"
+
+    timepoints = np.arange(1990.0, 2010.0 + 1.0, dtype=int)
+    start = create_test_df(
+        variables=[(f"variable_{i}", "Mt") for i in range(5)],
+        n_scenarios=3,
+        n_runs=6,
+        timepoints=timepoints,
+    )
+    assert start.columns.name is None
+
+    start.to_csv(out_path)
+
+    index_columns = ["variable", "scenario", "run", "unit"]
+
+    loaded = load_timeseries_csv(
+        out_path, index_columns=index_columns, out_columns_name=out_columns_name
+    )
+
+    assert loaded.columns.name == exp_columns_name
+
+
 @pytest.mark.xfail(reason="Not implemented")
 def test_load_timeseries_csv_infer_index_cols(tmp_path):
     # Suggested cases here: