format

Signed-off-by: Martijn Govers <[email protected]>

Author: mgovers

src/power_grid_model_io/converters/pandapower_converter.py

@@ -425,7 +425,7 @@ def _create_pgm_input_lines(self):
         pgm_lines["tan1"] = np.divide(
             self._get_pp_attr("line", "g_us_per_km", expected_type="f8", default=0),
             c_nf_per_km * (2 * np.pi * self.system_frequency * 1e-3),
-            where= c_nf_per_km != 0.0,
+            where=c_nf_per_km != 0.0,
         )
         pgm_lines["tan1"][np.equal(c_nf_per_km, 0.0)] = 0.0
         pgm_lines["i_n"] = (
@@ -439,7 +439,7 @@ def _create_pgm_input_lines(self):
         pgm_lines["tan0"] = np.divide(
             self._get_pp_attr("line", "g0_us_per_km", expected_type="f8", default=0),
             c0_nf_per_km * (2 * np.pi * self.system_frequency * 1e-3),
-            where= c0_nf_per_km != 0.0,
+            where=c0_nf_per_km != 0.0,
         )
         pgm_lines["tan0"][np.equal(c0_nf_per_km, 0.0)] = 0.0
         assert ComponentType.line not in self.pgm_input_data
@@ -1384,8 +1384,9 @@ def _pp_trafos_output(self):
         if self.trafo_loading == "current":
             ui_from = pgm_output_transformers["i_from"] * pgm_input_transformers["u1"]
             ui_to = pgm_output_transformers["i_to"] * pgm_input_transformers["u2"]
-            loading = (np.sqrt(3) * np.maximum(ui_from, ui_to) /
-                       pgm_input_transformers["sn"]) * loading_multiplier * 1e2
+            loading = (
+                (np.sqrt(3) * np.maximum(ui_from, ui_to) / pgm_input_transformers["sn"]) * loading_multiplier * 1e2
+            )
         elif self.trafo_loading == "power":
             loading = pgm_output_transformers["loading"] * loading_multiplier * 1e2
         else:
@@ -1716,9 +1717,8 @@ def join_currents(table: str, bus_name: str, i_name: str) -> pd.DataFrame:
             pp_switches_output.loc[link_ids, "i_ka"] = links["i_from"] * 1e-3
         in_ka = self.pp_input_data["switch"]["in_ka"].values
         pp_switches_output["loading_percent"] = np.nan
-        pp_switches_output["loading_percent"] = np.divide(pp_switches_output["i_ka"],
-                                                          in_ka, where= in_ka != 0)
-        
+        pp_switches_output["loading_percent"] = np.divide(pp_switches_output["i_ka"], in_ka, where=in_ka != 0)
+
         assert "res_switch" not in self.pp_output_data
         self.pp_output_data["res_switch"] = pp_switches_output
 

tests/unit/converters/test_pandapower_converter_output.py

@@ -770,7 +770,7 @@ def test_pp_switch_output():
                 "element": [10, 10, 10, 10, 10, 11, 11, 77, 88],
                 "et": ["t", "t", "t3", "t3", "t3", "l", "l", "b", "b"],
                 "closed": [True, True, True, True, True, True, True, True, True],
-                "in_ka": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], 
+                "in_ka": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
             },
             index=[40, 41, 42, 43, 44, 45, 46, 47, 48],
         ),

tests/validation/converters/test_pandapower_converter_input.py

@@ -117,6 +117,7 @@ def test_extra_info__serializable(extra_info):
     # Assert
     json.dumps(actual, cls=JsonEncoder)  # expect no exception
 
+
 @pytest.mark.filterwarnings("error:.*invalid value encountered in divide.*:RuntimeWarning")
 def test_pgm_input_lines__cnf_zero():
     pp_network = pp_net_3ph_minimal_trafo()
@@ -128,4 +129,3 @@ def test_pgm_input_lines__cnf_zero():
     pp_network.line.c0_nf_per_km = 0
     data, _ = pp_converter.load_input_data(pp_network)
     assert data[ComponentType.line]["tan0"] == 0
-    

tests/validation/converters/test_pandapower_converter_output.py

@@ -78,7 +78,7 @@ def load_validation_data_3ph(trafo_loading="power") -> PandaPowerData:
         return pp.file_io.from_json(PP_V2_NET_3PH_OUTPUT_FILE_CURRENT_LOADING)
 
 
-@pytest.fixture(params = ["power", "current"])
+@pytest.fixture(params=["power", "current"])
 def output_data(request) -> Tuple[PandaPowerData, PandaPowerData]:
     """
     Load the pandapower network and the json file, and return the output_data
@@ -88,7 +88,7 @@ def output_data(request) -> Tuple[PandaPowerData, PandaPowerData]:
     return actual, expected
 
 
-@pytest.fixture(params = ["power", "current"])
+@pytest.fixture(params=["power", "current"])
 def output_data_3ph(request) -> Tuple[PandaPowerData, PandaPowerData]:
     """
     Load the pandapower network and the json file, and return the output_data
@@ -264,7 +264,7 @@ def test_attributes(output_data: Tuple[PandaPowerData, PandaPowerData], componen
     pd.testing.assert_series_equal(actual_values, expected_values, atol=5e-4, rtol=1e-4)
 
 
-@pytest.mark.parametrize(("component", "attribute"), component_attributes_df(load_and_convert_pgm_data_3ph())) 
+@pytest.mark.parametrize(("component", "attribute"), component_attributes_df(load_and_convert_pgm_data_3ph()))
 def test_attributes_3ph(output_data_3ph: Tuple[PandaPowerData, PandaPowerData], component: str, attribute: str):
     """
     For each attribute, check if the actual values are consistent with the expected values for asym