Update pyproject.toml

pyproject.toml

@@ -29,7 +29,7 @@ h5py = ">=3.12.1,<4"
 pyarrow = ">=15.0.0,<21"
 httpx = ">=0.25.1,<1"
 pandas = ">=2.2.0,<3"
-pyspark = {extras = ["pandas-on-spark"], version = ">=4,<5"}
+pyspark = {extras = ["pandas-on-spark"], version = "4.0.0"}
 dataretrieval = ">=1.0.9,<2"
 numba = ">=0.60.0,<1"
 arch = ">=7.0.0,<8"

src/teehr/evaluation/evaluation.py

@@ -193,8 +193,7 @@ def fetch(self) -> Fetch:
     @property
     def metrics(self) -> Metrics:
         """The metrics component class for calculating performance metrics."""
-        cls = Metrics(self)
-        return cls()
+        return Metrics(self)
 
     @property
     def units(self) -> UnitTable:

src/teehr/evaluation/metrics.py

@@ -65,6 +65,8 @@ def __call__(
         >>> ev = teehr.Evaluation()
         >>> metrics = ev.metrics(table_name="primary_timeseries")
         """
+        logger.info(f"Initializing Metrics for table: {table_name}.{namespace_name or ''}{'.' if namespace_name else ''}{catalog_name or ''}")
+
         self.table_name = table_name
         self.table = self._ev.table(
             table_name=table_name,

src/teehr/evaluation/tables/base_table.py

@@ -53,6 +53,7 @@ def __call__(
         catalog_name: Union[str, None] = None
     ) -> "Table":
         """Initialize the Table class."""
+        logger.info(f"Initializing Table for table: {table_name}.{namespace_name or ''}{'.' if namespace_name else ''}{catalog_name or ''}")
         self.table_name = table_name
         self.sdf = None
         tbl_props = TBLPROPERTIES.get(table_name)

tests/query/test_metrics_query.py

@@ -51,7 +51,7 @@ def test_executing_deterministic_metrics(tmpdir):
     # Get the currently available fields to use in the query.
     flds = ev.joined_timeseries.field_enum()
 
-    metrics_df = ev.metrics.query(
+    metrics_df = ev.metrics().query(
         include_metrics=include_nonconditional_metrics,
         group_by=[flds.primary_location_id],
         order_by=[flds.primary_location_id],
@@ -75,7 +75,7 @@ def test_executing_deterministic_metrics(tmpdir):
         if callable(func) and func().attrs.get('requires_threshold_field', True)  # noqa
     ]
 
-    metrics_df = ev.metrics.add_calculated_fields([
+    metrics_df = ev.metrics().add_calculated_fields([
         tcf.AbovePercentileEventDetection(
             skip_event_id=True,
             add_quantile_field=True,
@@ -105,7 +105,7 @@ def test_executing_signatures(tmpdir):
     # Get the currently available fields to use in the query.
     flds = ev.joined_timeseries.field_enum()
 
-    metrics_df = ev.metrics.query(
+    metrics_df = ev.metrics().query(
         include_metrics=include_all_metrics,
         group_by=[flds.primary_location_id],
         order_by=[flds.primary_location_id],
@@ -142,7 +142,7 @@ def test_metrics_filter_and_geometry(tmpdir):
         )
     ]
 
-    metrics_df = ev.metrics.query(
+    metrics_df = ev.metrics().query(
         include_metrics=include_metrics,
         group_by=[flds.primary_location_id],
         order_by=[flds.primary_location_id],
@@ -161,7 +161,7 @@ def test_metric_chaining(tmpdir):
     ev = setup_v0_3_study(tmpdir)
 
     # Test chaining.
-    metrics_df = ev.metrics.query(
+    metrics_df = ev.metrics().query(
         order_by=["primary_location_id", "month"],
         group_by=["primary_location_id", "month"],
         include_metrics=[
@@ -302,7 +302,7 @@ def test_ensemble_metrics(tmpdir):
     crps.reference_configuration = "benchmark_forecast_hourly_normals"
 
     include_metrics = [crps]
-    metrics_df = ev.metrics.query(
+    metrics_df = ev.metrics().query(
         include_metrics=include_metrics,
         group_by=[
             "primary_location_id",
@@ -341,21 +341,21 @@ def test_metrics_transforms(tmpdir):
     mvtd_t.transform = 'log'
 
     # get metrics_df
-    metrics_df_tansformed_e = test_eval.metrics.query(
+    metrics_df_tansformed_e = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             kge_t_e,
             mvtd_t
         ]
     ).to_pandas()
-    metrics_df_transformed = test_eval.metrics.query(
+    metrics_df_transformed = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             kge_t,
             mvtd_t
         ]
     ).to_pandas()
-    metrics_df = test_eval.metrics.query(
+    metrics_df = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             kge,
@@ -396,7 +396,7 @@ def test_metrics_transforms(tmpdir):
     )
 
     # get metrics df control and assert divide by zero occurs
-    metrics_df_e_control = test_eval.metrics.query(
+    metrics_df_e_control = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             r2,
@@ -407,7 +407,7 @@ def test_metrics_transforms(tmpdir):
     assert np.isnan(metrics_df_e_control.pearson_correlation.values).all()
 
     # get metrics df test and ensure no divide by zero occurs
-    metrics_df_e_test = test_eval.metrics.query(
+    metrics_df_e_test = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             r2_e,
@@ -436,7 +436,7 @@ def test_metrics_transforms(tmpdir):
     )
 
     # get metrics df control and assert divide by zero occurs
-    metrics_df_e_control = test_eval.metrics.query(
+    metrics_df_e_control = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             r2,
@@ -447,7 +447,7 @@ def test_metrics_transforms(tmpdir):
     assert np.isnan(metrics_df_e_control.pearson_correlation.values).all()
 
     # get metrics df test and ensure no divide by zero occurs
-    metrics_df_e_test = test_eval.metrics.query(
+    metrics_df_e_test = test_eval.metrics().query(
         group_by=["primary_location_id", "configuration_name"],
         include_metrics=[
             r2_e,