Merge 23155f9 into 80e8b2a

.coveragerc

@@ -3,15 +3,5 @@
 # Regexes for lines to exclude from consideration
 exclude_lines =
     if __name__ == .__main__.:
-    raise
-    print
-    warn
-    plt.show()
-    message =
-    log.warning
-    get_logger().warning
-    log.error
-    get_logger().error
-
 
 ignore_errors = True

optim_esm_tools/analyze/clustering.py

@@ -48,7 +48,7 @@ def build_clusters(
         db_fit = DBSCAN(eps=max_distance_km / 6371.0, **cluster_opts).fit(
             X=coordinates_rad, sample_weight=weights
         )
-    except ValueError as e:
+    except ValueError as e:  # pragma: no cover
         raise ValueError(
             f'With {coordinates_rad.shape} and {getattr(weights, "shape", None)} {coordinates_rad}, {weights}'
         ) from e
@@ -181,7 +181,7 @@ def _check_input(data, lat_coord, lon_coord):
         # In an older version, this would have been the default.
         lat, lon = lat_coord, lon_coord
 
-    if data.shape != lon.shape or data.shape != lat.shape:
+    if data.shape != lon.shape or data.shape != lat.shape:  # pragma: no cover
         message = f'Wrong input {data.shape} != {lon.shape, lat.shape}'
         raise ValueError(message)
     return lat, lon
@@ -192,7 +192,9 @@ def _build_cluster_with_kw(lat, lon, show_tqdm=False, **cluster_kw):
     masks = []
     clusters = [np.rad2deg(cluster) for cluster in build_clusters(**cluster_kw)]
     if lat.shape != lon.shape:
-        raise ValueError(f'Got inconsistent input {lat.shape} != {lon.shape}')
+        raise ValueError(
+            f'Got inconsistent input {lat.shape} != {lon.shape}'
+        )  # pragma: no cover
     for cluster in clusters:
         mask = np.zeros(lat.shape, np.bool_)
         for coord_lat, coord_lon in tqdm(
@@ -273,7 +275,7 @@ def _calculate_distance_map(lat, lon):
                     continue
                 alt_coord = (lat[alt_lat], lon[alt_lon])
                 if alt_coord == current:
-                    continue
+                    raise ValueError('How can this happen?')  # pragma: no cover
                 neighbors[i] = _distance_bf_coord(*current, *alt_coord)
             distances[lat_i][lon_i] = np.max(neighbors)
     return distances
@@ -286,7 +288,7 @@ def _distance(coords, force_math=False):
             from geopy.distance import geodesic
 
             return geodesic(*coords).km
-        except (ImportError, ModuleNotFoundError):
+        except (ImportError, ModuleNotFoundError):  # pragma: no cover
             pass
     if len(coords) != 4:
         coords = [c for cc in coords for c in cc]

optim_esm_tools/analyze/cmip_handler.py

@@ -46,7 +46,9 @@ def add_conditions_to_ds(
     if len(set(desc := (c.short_description for c in calculate_conditions))) != len(
         calculate_conditions
     ):
-        raise ValueError(f'One or more non unique descriptions {desc}')
+        raise ValueError(
+            f'One or more non unique descriptions {desc}'
+        )  # pragma: no cover
     if condition_kwargs is None:
         condition_kwargs = dict()
 
@@ -117,7 +119,7 @@ def read_ds(
     )
 
     if not isinstance(variable_of_interest, str):
-        raise ValueError('Only single vars supported')
+        raise ValueError('Only single vars supported')  # pragma: no cover
     if kwargs:
         log.error(f'Not really advised yet to call with {kwargs}')
         _cache = False
@@ -137,11 +139,11 @@ def read_ds(
     if os.path.exists(res_file) and _cache:
         return oet.analyze.io.load_glob(res_file)
 
-    if not os.path.exists(data_path):
+    if not os.path.exists(data_path):  # pragma: no cover
         message = f'No dataset at {data_path}'
         if strict:
             raise FileNotFoundError(message)
-        warn(message)
+        log.warning(message)
         return None
 
     if pre_process:

test/test_clustering.py

@@ -79,6 +79,11 @@ def test_geopy_alternative():
         clustering._distance(coords),
         rtol=0.1,
     )
+    assert np.isclose(
+        clustering._distance_bf_coord(*flat_coord),
+        clustering._distance(coords, force_math=True),
+        rtol=0.1,
+    )
 
 
 def test_infer_step_size():

test/test_workflow.py

@@ -60,6 +60,14 @@ def test_map_maker_time_series(self, **kw):
     def test_map_maker_time_series_only_running_mean(self):
         self.test_map_maker_time_series(only_rm=True)
 
+    def read_w_no_cache(self):
+        oet.read_ds(
+            os.path.split(self.ayear_file)[0],
+            _file_name=self.name_merged,
+            apply_transform=False,
+            pre_process=False,
+        )
+
     def test_date_out_of_range(self):
         with self.assertRaises(oet.analyze.pre_process.NoDataInTimeRangeError):
             oet.read_ds(